“I’m not coming to be a cancer patient,” Balti said at a press conference for the event I could have been lying in bed feeling sorry for myself I want to be a celebration of life tonight.” Daniele Venturelli/Getty ImagesDaniele Venturelli/Getty ImagesDaniele Venturelli/Getty ImagesShe wore a suite of designer dresses (Valentino but it was the silver Roberto Cavalli gown that brought the crowd to their feet The dress had a disco-era cutout that perfectly displayed her exposed belly and post-surgery scars She has noted that many brands she worked with in the past have not reached out since her diagnosis “I want to show the world the power of the look I have now,” she said Balti shows us there is beauty in even the most challenging and frightening phases of life she reminds everyone whose body has been changed by illness that they are still worthy of wearing the dress and having fun at the party—an important reminder The Naked Manicure Trend Will Be Everywhere This Spring Victoria Beckham Takes Beauty Inspo From the Olsens 2025’s Trending Eyeliner Flatters Those in Their 20s and 70s Alike Sign up for Vogue’s beauty newsletter to receive the insider’s guide to all things beauty and wellness Never miss a Vogue moment and get unlimited digital access for just $2 $1 per month the Pakistani dish born in BirminghamFound on menus worldwide this mildly spiced curry takes inspiration from Pakistani cuisine but was born far from the Kashmiri mountains This article was produced by National Geographic Traveller (UK).It was during the mid-1970s in Birmingham that balti was born where South Asian culinary culture is influenced by the western palate the sweet and spicy curry dish fast became a permanent fixture on menus it’s so adored in the Midlands that there’s even been calls to officially give it Traditional Speciality Guaranteed (TSG) status as part of the European Protected Food Names scheme Balti was first introduced to Birmingham around 1975 by a local Pakistani restaurateur in an effort to appeal to westerners with a fast-cooked “He wanted to attract white Brummies and knew they would want quick service and meat off the bone as opposed to the Pakistani custom [of meat on the bone],” says local author and historian Andy Munro Andy grew up in what’s become known as the Balti Triangle area of southeast Birmingham and estimates that he’s consumed more than 2,000 baltis over his lifetime which was popular in the mountainous areas of Kashmir.” Balti was introduced to Birmingham in the 1970s by a Pakistani restaurateur who wanted to create a dish suited to mild western tastes Today it's served at restaurants including Shababs in the city's Balti Triangle district.Photograph by Ben Rowe (Top) (Left) and Photograph by Ben Rowe (Bottom) (Right)To perfect his Pakistani-Kashmiri cooking the restaurateur had a wok-style bowl specially designed in Birmingham — known today as a balti bowl It was made using pressed steel which heats up quickly with a flat bottom for stability on the stove and flat handles that allow easier manoeuvrability both on the stove and on the table where it’s served directly to the customer authentic balti bowls are still manufactured in Birmingham The balti is a simple dish but must be cooked fast over a high flame to create a crispy It’s unique from other types of curry as it’s served and eaten out of the same balti bowl it’s cooked in Protein and ingredient quantities vary from restaurant to restaurant a pinch of salt and fresh coriander to garnish An authentic balti has a sweet twang and a mild spicy kick and is best mopped up out of the bowl with a pillowy-soft naan fresh from the oven This is sometimes colloquially known as the ‘Birmingham scoop’ “Although curry came from India and Pakistan the balti was born in Birmingham,” says Zaf Hussain head chef at one of the city’s most iconic balti house “And while curries are normally more like a stew The caramelised onions give it a natural sweetness and it’s cooked with vegetable oil as opposed to ghee — traditionally used in India — which helps it cook faster over a high flame.” The balti has been much replicated in modern times crisps and nuts all claiming to bear the iconic Birmingham flavour But a balti’s true character will never be captured in variants like these as the essence of the dish relies on it being cooked quickly over a strong flame this local institution has grown from 22 covers to 122 and its bring-your-own-booze policy helps keep costs affordable for customers perennial favourites include the lamb tikka and tandoori chicken wings 2. Shahi Nan KababJust a 10-minute stroll from Shababs Shahi Nan Kabab has been serving balti since 1984 and comes highly rated by those in the know (including Andy Munro) earned his stripes cooking for the Pakistani navy and make sure you leave room for a garlic naan to Birmingham scoop the main event either observed and verified firsthand by the reporter or reported and verified from knowledgeable sources Translations may contain inaccuracies—please refer to the original content Italian model Bianca Balti has taken to her Instagram account to reveal that she has been diagnosed with stage 3C ovarian cancer According to the Ovarian Cancer Research Alliance this is a type of ovarian cancer where "cancer is in one or both ovaries or fallopian tubes and it has visibly spread to organs outside the pelvic region with deposits larger than 2cm." There is also the possibility that the cancer spreads to the lymph nodes in the abdomen or to the surface of the liver or spleen The 40-year-old Sports Illustrated Swimsuit model shared the news on Sunday which included various pictures and videos from a recent hospital visit Newsweek emailed a spokesperson for Balti for comment on Monday "Last Sunday, I checked myself into the ER to find out that my lower abdominal pain was stage 3C ovarian cancer," Balti wrote A post shared by instagram She continued: "It's been a week full of fear my loved ones (my daughters are at the top of the list) you can borrow some of mine cause I have loads cancer has given me a chance to find beauty through life's hurdles." there are various photos and videos of her in a hospital bed while friends and family visit took to the comments to share their support for the model You're a warrior ❤️❤️," model Shanina Shaik wrote "Oh B❤️🫂 sending you so much love and strength x," commented Gigi Hadid Influencer Anda Gentile added: "Can't wait to go on walks with you 💪♥️." After genetic testing revealed Balti is a carrier for the BRCA1 gene—a gene that raises your breast cancer risk—she underwent a preventive bilateral mastectomy in December 2022 She opened up about the operation in a March 2024 interview with Vogue yet—but I knew the chances were much higher that I would be one day And I now had the free will to do something about it," she said "The hardest part of this entire process has been the fear how we look should be the last of our concerns My relationship didn't change with my body throughout the process Balti explained that every time she has spoken publicly about her experience someone has contacted her to let her know they've had a mammogram or tested their genes She added: "So I know it actually makes a difference when you talk about it." Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground Newsletters in your inbox See all who was named Sports Illustrated Swimsuit Rookie of the Year in 2017 15 that she'd been diagnosed with stage 3 ovarian cancer Bianca Balti is sharing a heartbreaking health update The model, 40, announced in a Sept. 15 Instagram post that she had been diagnosed with ovarian cancer. "Last Sunday, I checked myself into the ER to find out that my lower abdominal pain was stage 3C ovarian cancer," Balti wrote. "It’s been a week full of fear, pain and tears but mostly love, hope, laughter, and strength." The Sports Illustrated Swimsuit model has an optimistic outlook on her cancer battle, sharing that her friends and family serve as motivation to give this fight everything she has. "I have a long journey ahead, but I know I will beat this," she said. "For myself, my loved ones (my daughters are at the top of the list), and all of you who need strength, you can borrow some of mine cause I have loads." Balti—who shares Matilde, 17, with her ex-husband, Christian Lucidi, and Mia, 9, with her husband Matthew McRae—concluded her health update with an inspiring message to her followers. "Life happens; give it a reason," she shared. "So far, cancer has given me a chance to find beauty through life’s hurdles." As part of her announcement, the Italian model also included a slew of smile-filled selfies and videos from her time in the hospital with her loved ones and medical staff. View this post on Instagram A post shared by Bianca Balti (@biancabalti) Balti further elaborated on her positive outlook as she tackles cancer and I'm just blessed that I found out," she explained to the camera adding that she had just left surgery after "they took out everything" from her "lower abdomen," adding that she's gearing up for chemotherapy and you'll see me soon in perfect health." This isn't the first time that Balti has publicly opened up about her health she shared that she had undergone a preventative double mastectomy in Dec 2022 after learning that she was a carrier for the BRCA1 gene The results meant that she had a 50 percent chance of getting breast cancer and a 30 perfect chance of getting ovarian cancer in her lifetime "The hardest part of this entire process has been the fear,” she told Vogue in March Balti said that hearing from other women who were inspired to get checked after learning her story is what pushed her to continue to be open about her health journey "Every time I speak publicly about what happened to me people will reach out and tell me they did a mammogram or had their genes tested,” she explained “So I know it actually makes a difference when you talk about it." The 'Sports Illustrated Swimsuit' model shared a health update on Instagram on Sunday “I know I will beat this,” Balti said in September, and last night she radiated positivity on stage as a co-host of Italy’s Sanremo music festival. “I’m not coming to be a cancer patient,” the mother of two daughters said at a press conference ahead of the event. “I don’t want to tell about the pain. I could have been lying in bed feeling sorry for myself, but instead, I want to be a celebration of life tonight.” who has lost her hair while undergoing treatment appeared in a series of designer gowns: a custom powder-blue Valentino gown by new creative director Alessandro Michele a shimmering Armani Privé number covered in midnight-blue sequins a sheer Fendi Couture dress and a Roberto Cavalli halter-neck with a cut-out at the waist give it a reason,” Balti said following her diagnosis cancer has given me a chance to find beauty through life’s hurdles.” She previously said: “I want to show the world the power of the look I have now.” Her beaming smile on stage was powerful indeed Everything You Need To Know About The Met Gala 2025 Join The Vogue Newsletter For The Latest Fashion, Beauty And Street Style Trends Straight To Your Inbox Read Billie Eilish’s May 2025 Cover Interview In Full The Key Spring/Summer 2025 Trends To Know Now Join British Vogue’s Met Gala Community Ahead Of Fashion’s Biggest Night. This link redirects to a third-party website. The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference. The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer’s disease. Volume 18 - 2024 | https://doi.org/10.3389/fncom.2024.1444019 Introduction: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline highlighting the critical need for early diagnosis and intervention to improve patient outcomes Timely detection plays a crucial role in managing the disease more effectively Pretrained convolutional neural networks (CNNs) trained on large-scale datasets providing a head start for developing more accurate models Methods: This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks We validated this model using a large dataset of MRI images from AD patients Performance was evaluated in terms of classification accuracy and robustness against noise and the results were compared to several commonly used models in AD detection Results: The proposed hybrid model demonstrated significant performance improvements over individual models achieving an accuracy classification rate of 99.85% Comparative analysis with other models further revealed the superiority of the new architecture particularly in terms of classification rate and resistance to noise interference Discussion; The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection By improving feature representation through the combination of two pretrained networks this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression This approach holds promise for aiding in timely diagnoses and treatment decisions contributing to better management of Alzheimer’s disease Alzheimer’s disease (AD) is a degenerative neurological condition marked by cognitive decline In order to manage the illness and enhance the patients’ quality of life because the structural alterations in Alzheimer’s disease are subtle and complex precisely identifying the condition using MRI scans remains a substantial issue Brain MRI pictures show subtle structural differences that are difficult to interpret without sophisticated tools and conventional diagnostic procedures frequently fail to capture these complex patterns In order to support healthcare practitioners and automated classification systems are required This is because early and accurate diagnosis might be challenging The complex patterns in MRI scans that differentiate between different stages of Alzheimer’s disease are difficult for traditional machine learning models and even some deep learning architectures to grasp and cross-dataset generalization are not sufficiently addressed by many of the models that are currently in use More advanced models are therefore required in order to efficiently learn and generalize these patterns resulting in classifications that are more accurate Though promising in other medical imaging tasks advanced deep learning models are still limited in their ability to classify AD patients because of the particular difficulties presented by the disease’s course and its subtle impacts on brain structure This justifies the exploration of novel model architectures and combinations to push the boundaries of what is currently achievable in AD diagnosis we present a combinatorial deep learning method that combines DenseNet121 and Xception DenseNet121’s dense connection makes feature reuse and gradient flow more effective Because every layer in DenseNet121 is feed-forward coupled to every other layer the vanishing gradient issue is lessened and feature reuse is encouraged Xception lowers model parameters and improves computational performance thanks to its depthwise separable convolutions The convolution procedure is broken down into two parts in this architecture: a depthwise convolution and a pointwise convolution This results in a significant reduction in computational cost without sacrificing performance we aim to leverage the strengths of both architectures resulting in a model that is both powerful and efficient with an average improvement in accuracy of 10% over the previous approaches The effective fusion of Xception’s efficient convolutions and DenseNet121’s dense connectivity is responsible for this improvement By using the Synthetic Minority Over-sampling Technique (SMOTE) which addressed the dataset’s class imbalance problem this performance improvement was further improved In order to balance the dataset without just copying existing samples SMOTE creates synthetic samples for the minority class by interpolating between existing samples The model can learn more efficiently thanks to this balanced dataset particularly when some stages of Alzheimer’s disease are underrepresented in the training set Considerable progress has been made in the categorization of Alzheimer’s disease from MRI scans using the suggested combinatorial strategy Utilizing the advantages of both Xception and DenseNet121 our model offers a reliable and effective solution While Xception’s computational efficiency enables faster and more resource-efficient training and inference DenseNet121’s dense connections enable a better and more thorough grasp of the subtle aspects of MRI images SMOTE integration makes the model more robust in real-world applications by improving its generalization across imbalanced datasets These developments open the door to earlier and more precise Alzheimer’s disease diagnosis which may improve patient outcomes and facilitate the development of more potent disease management techniques This study highlights the potential of combinatorial deep learning approaches in overcoming existing limitations and sets the stage for future research in this critical area This paper is structured as follows: we explain in section 2 some State-Of-the-Art Models Some mathematical formulations of the proposed model are described in Section 3 as well as the proposed model architecture the proposed model’s architecture will be presented Section 4 details the results achieved in this study the implications of our findings and future directions for research are exposed Our approach demonstrates the potential of the proposed hybrid model in enhancing Alzheimer’s research providing a framework that can be extended to other medical imaging applications we propose to integrate Xception and DenseNet121 two newly modified pretrained deep learning networks the suggested model has the highest accuracy These works show how transfer learning can be used to improve the accuracy of image classification in the Alzheimer’s dataset and show how transfer learning can be used to further develop deep learning-based techniques The dataset of MRI images (link in Data Availability statement) initially consists of two parts: Training and Testing images, each with over 5000 images classified according to the severity of Alzheimer’s disease. Figure 1 shows some AD images categorized into four classes: Very Mild Demented Using the same methodologies as the proposed architecture a comparison study with other pretrained networks has been conducted to assess the performance of the suggested model All the dataset images were preprocessed as follows: – Splitting data: the dataset contains two files: train and test we merge the two parts and split obtained data into ratios of 80% for training and 20% for testing – Resizing Images: All images were resized to reduce computational power consumption and speed up application execution we ensured that the model could process the data more efficiently leading to faster training times without compromising accuracy – Data Augmentation: Data augmentation techniques were employed to create new training datasets that are variations of the original images This process helps prevent overfitting by exposing the model to a wider variety of data • Rotation: Rotating images helps the model become invariant to the orientation of the MRI scans allowing it to learn features regardless of image alignment • Flipping (Horizontal and Vertical): Flipping images increases data diversity by creating mirror images which helps the model learn to recognize features from different perspectives • Shifting (Width and Height): Shifting images horizontally and vertically helps the model become invariant to small positional changes in the MRI scans • Zoom: Applying zoom augmentation ensures the model can handle variations in the size of brain structures helping it focus on different levels of detail • Brightness Adjustment: Adjusting brightness variations makes the model robust to changes in lighting conditions ensuring consistent performance across different MRI scans Example of images belongs to four classes from Alzheimer’s dataset These augmentation techniques not only increase the quantity of training data but also enhance the model’s ability to generalize by learning from a more diverse set of images This diversity helps improve the model’s robustness and accuracy in identifying features relevant to different stages of Alzheimer’s Disease – Oversampling with SMOTE: The Synthetic Minority Oversampling Technique (SMOTE) was used by Chawla et al. (2002) and applied in this study to address the issue of unbalanced classes SMOTE generates synthetic samples for the minority class by interpolating between existing samples The aim of this study is to merge these two PN If AccHybrid is higher than AccDenseNet121 and AccXception it indicates that the hybrid model is more accurate in its predictions The hybrid model combines features from both DenseNet121 and Xception potentially leveraging their complementary strengths which can result in improved classification performance compared to each individual model a higher accuracy for the hybrid model shows that it has a better overall performance in correctly identifying samples proving its superiority over the individual models ΔAccHybrid : Improvement in accuracy of the hybrid model The improvement in ΔAccHybrid measures how much the accuracy of the hybrid model exceeds that of the best-performing individual model (either DenseNet121 or Xception) By subtracting the maximum accuracy of the individual models from the accuracy of the hybrid model we can quantify the enhancement in performance provided by the hybrid approach A positive value of indicates that the hybrid model offers superior accuracy compared to the single best model demonstrating its effectiveness in improving classification results beyond what each individual model alone can achieve The ensemble performance gain GAcc calculates the average improvement in accuracy provided by the hybrid model compared to each individual model It is determined by averaging the differences in accuracy between the hybrid model and DenseNet121 This calculation helps quantify the extent to which the hybrid model outperforms both individual models A positive GAcc value signifies that the hybrid model achieves better accuracy than either DenseNet121 or Xception thereby demonstrating the advantages of combining the strengths of both models This gain highlights the effectiveness of the hybrid approach in improving classification accuracy beyond what is achieved by each individual model alone Error reduction ΔE measures how much the hybrid model’s error rate is reduced compared to the best-performing individual model It is calculated by subtracting the error rate of the hybrid model from the minimum error rate of DenseNet121 and Xception This metric helps to quantify the improvement in classification performance of the hybrid model by showing that it has a lower error rate than the best individual model A lower error rate in the hybrid model indicates enhanced accuracy and effectiveness in classification providing evidence that combining the two models leads to better overall performance compared to relying on either model alone This reduction in error underscores the advantage of the hybrid approach in minimizing misclassifications DAcc,i : Difference in accuracy for the i-th sample DA⁢c⁢c,i¯ : Mean difference in accuracy sD,Acc : Standard deviation of the differences in accuracy To determine the statistical significance of the hybrid model’s performance improvement DAcc,i measures the accuracy difference between the hybrid model and the best individual model for each sample showing how much better the hybrid model performs on a sample-by-sample basis The mean difference in accuracy DA⁢c⁢c¯ averages these differences across all samples The standard deviation of these differences sD,Acc assesses the variability of the improvements with a low value suggesting consistent superiority the t-value tAcc assesses the statistical significance of the mean accuracy improvement by comparing DA⁢c⁢c¯ to its standard error A high t-value confirms that the observed improvement in accuracy is statistically significant demonstrating the hybrid model’s robustness and superiority over the individual models The architecture combines feature extraction from these pre-trained models with additional convolutional layers, upsampling, and fully connected layers to optimize the model’s performance. Below is a detailed explanation of each sub-module in the architecture presented by Figure 2: The DenseNet121 model is used as a feature extractor but its weights are “frozen” meaning they are not updated during training This allows the model to leverage pre-trained weights without modifying them The output of the frozen DenseNet121 model is passed to the next convolutional layer the Xception model is also frozen and used as a feature extractor The frozen model’s output is sent to a convolutional layer • Convolution Layer (for DenseNet121 and Xception): After extracting features from both DenseNet121 and Xception these features are further processed by convolutional layers The convolution layers apply filters to extract spatial hierarchies from the feature maps These layers help in refining the features obtained from the pre-trained models • UpSampling Layer (for DenseNet121 and Xception): Once features are processed through the convolution layers the UpSampling layers increase the resolution of the feature maps (essentially scaling them up) This is likely done to match the spatial dimensions of the two models’ feature maps before concatenating them The outputs from the upsampled DenseNet121 and Xception models are concatenated along the channel dimension This step fuses the feature representations from both models combining their strengths to form a more comprehensive feature set for the next stage of the model the fused feature map is passed through a fully connected (dense) layer This layer serves to learn and capture more abstract relationships in the combined features transforming the spatial features into more compact Dropout is applied to prevent overfitting by randomly setting a fraction of the input units to 0 during training This forces the model to not rely on any one specific feature and improves generalization Another fully connected layer follows the dropout This layer further transforms the features likely preparing them for the final classification It’s part of the final layers that learn high-level representations to differentiate between the classes Global average pooling reduces the spatial dimensions of the feature maps by averaging them across each channel This reduces the dimensionality of the output producing a single value per feature map (or channel) It replaces traditional fully connected layers before the output promoting model generalization and reducing the risk of overfitting The final output layer produces the class probabilities for the input data This layer takes the reduced representation from the global average pooling layer and outputs a prediction likely using softmax activation for multi-class classification in Alzheimer’s disease MRI image classification are used to assess how well deep learning architecture’s function The Area Under the Curve (AUC) is an effective metric having values in the interval [0 Since there is perfect discrimination between instances of the two classes when all Benign cases are classified as Malignant InceptionV3&DenseNet121 and Xception&DenseNet121 of Alzheimer’s Dataset Take note that there are fewer False Positive and False Negative cases in the confusion matrix of Xception&DenseNet121 where (a) confusion matrix of DenseNet121 model (d) confusion matrix of InceptionV2&DenseNet121 model (e) confusion matrix of Xception&DenseNet121 model in DenseNet121 model and the Xception model Accuracy and loss curves for different models Comparison between two proposed approaches (with and without SMOTE Technique) Comparison between the proposed model and state of the art models Comparison between the proposed model and state of the art models applied on MRI images from ADNI dataset Exploring the impact of Gaussian Blur (σ = 0.2 0.3) on Alzheimer’s dataset image classification for each class Impact of various noise types on Alzheimer’s dataset image classification: all predicted classes match real classes UMAP projection of extracted features: visualization of class separation in each model The proposed hybrid model integrates DenseNet121 and Xception architectures leveraging their unique strengths to improve the classification of Alzheimer’s disease using MRI images The approach centers on freezing the pre-trained layers of these models to retain their pre-learned features while preventing the model from becoming overly complex or prone to overfitting This combination of architectures is particularly appropriate for medical imaging tasks due to their proven success in domains such as breast and brain image classification the model benefits from the robust feature extraction capabilities of DenseNet121 and Xception while still allowing the new layers to adapt specifically to Alzheimer’s MRI data An essential enhancement to this approach is the application of SMOTE (Synthetic Minority Over-sampling Technique) which addresses class imbalance by generating synthetic examples for underrepresented classes This results in a more balanced training dataset allowing the model to better generalize across all classes The model’s performance improvement especially in correctly classifying minority classes demonstrates the effectiveness of SMOTE in reducing bias and improving overall accuracy This method enhances the model’s ability to identify complex features in the minority classes which is crucial for a more equitable classification performance the model’s ability to remain robust under noisy conditions such as Gaussian blur and salt-and-pepper noise showcases its reliability for real-world medical imaging applications Noise is an inherent challenge in medical images due to factors like equipment limitations or patient movement and a model that can maintain high accuracy under these conditions is vital for clinical deployment The robustness observed across various noise types highlights the model’s resilience underscoring its potential for reliable clinical use which balances DenseNet121’s dense connections and Xception’s computational efficiency through depthwise separable convolutions contributes to an effective extraction and processing of features from MRI images This synergy between the two models results in a powerful classifier that captures complex patterns while maintaining reasonable computational costs the increased complexity and resource demands present challenges particularly in terms of training time and memory requirements Future research could explore optimization techniques like pruning and quantization to reduce model size and improve efficiency without sacrificing performance the proposed hybrid model effectively capitalizes on the strengths of both DenseNet121 and Xception while addressing key challenges like class imbalance and noise robustness While the model exhibits improved performance and robustness future work should focus on further optimization and exploration of complementary architectures to enhance computational efficiency and scalability for broader clinical application we used a large Alzheimer’s disease (AD) dataset to carefully assess the performance of five different models: DenseNet121 The most notable accomplishment is the exceptional accuracy of our model which achieved an astounding 99.85% inside the AD dataset This notable enhancement marks a major advancement in our newly suggested architecture’s ability to classify and detect things By utilizing these cutting-edge neural networks we support the continuous endeavors to improve patient care and early AD diagnosis Our results highlight the potential utility of transfer learning techniques in medical imaging underscoring the significance of ongoing innovation in the battle against Alzheimer’s – Further Validation on External Datasets: Although the model performs remarkably well on the current AD dataset it is essential to validate its generalizability by testing it on external and more diverse datasets particularly those from different populations or imaging sources – Incorporate Explainability Methods: For clinical adoption incorporating explainability techniques (e.g. SHAP) would help provide insights into the decision-making process of the model ensuring transparency and fostering trust among healthcare professionals – Optimization for Real-Time Deployment: Investigating lightweight versions of the hybrid model could make it more suitable for real-time deployment in clinical settings where computational resources may be limited or the integration of more efficient architectures like MobileNetV2 – Broader Clinical Applications: Given the success in Alzheimer’s classification the hybrid architecture could be adapted and tested for other neurodegenerative diseases such as Parkinson’s and Huntington’s disease thereby broadening its impact on early diagnosis across a range of conditions – Ongoing Research in Transfer Learning: Continuous refinement of transfer learning approaches should be pursued to stay ahead in the rapidly evolving field of medical imaging Exploration of novel pre-training strategies on larger more diverse medical datasets could further boost performance in specialized tasks The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images The author(s) declare that no financial support was received for the research The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest The reviewer [ZM] declared a shared affiliation with the authors to the handling editor at the time of review All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher “Alzheimer’s disease MRI classification using EfficientNet: A deep learning model,” in Proceedings of the 2024 4th international conference on applied artificial intelligence (ICAPAI) Google Scholar Alzheimer’s disease diagnosis and classification using deep learning techniques PubMed Abstract | Crossref Full Text | Google Scholar Advanced integration of machine learning techniques for accurate segmentation and detection of Alzheimer’s disease Google Scholar Hybridized deep learning approach for detecting Alzheimer’s disease Hippocampus segmentation-based Alzheimer’s disease diagnosis and classification of MRI images Medical image classification for Alzheimer’s using a deep learning approach Crossref Full Text | Google Scholar SMOTE: Synthetic minority over-sampling technique Google Scholar Transfer learning for Alzheimer’s disease diagnosis using efficientnet-B0 convolutional neural network Google Scholar “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition Google Scholar “Alzheimer’s disease classification for MRI images using convolutional neural networks,” in Proceedings of the 2024 6th International Conference on Computing and Informatics (ICCI) Google Scholar AlexNet and DenseNet-121-based hybrid CNN architecture for skin cancer prediction from dermoscopic images Google Scholar An approach for classification of Alzheimer’s disease using deep neural network and brain magnetic resonance imaging (MRI) Google Scholar A review on evaluation metrics for data classification evaluations Google Scholar Identifying Alzheimer disease dementia levels using machine learning methods Google Scholar A transfer learning approach for multiclass classification of Alzheimer’s disease using MRI images Adam: A method for stochastic optimization Google Scholar “Classification of Alzheimer disease from MRI image using combination naïve bayes and invariant moment,” in Proceedings of the 5th international conference on applied engineering Google Scholar “Exploring deep transfer learning ensemble for improved diagnosis and classification of alzheimer’s disease,” in Proceedings of the international conference on brain informatics Google Scholar Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods Machine learning algorithms for the diagnosis of Alzheimer and Parkinson disease K-Net based segmentation and manta crow light spectrum optimization enabled DNFN for classification of Alzheimer’s disease using MRI images Google Scholar Alzheimer disease classification through transfer learning approach shallow and ensemble machine learning methods for the automated classification of Alzheimer’s disease Classification of Alzheimer’s disease using transfer learning and support vector machine Google Scholar Performance analysis of machine learning and deep learning models for classification of Alzheimer’s disease from brain MRI Google Scholar Alzheimer’s disease classification with a cascade neural network Pyramid-attentive GAN for multimodal brain image complementation in Alzheimer’s disease classification Google Scholar Abid S and Sayadi M (2024) A combinatorial deep learning method for Alzheimer’s disease classification-based merging pretrained networks Copyright © 2024 Slimi, Balti, Abid and Sayadi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) distribution or reproduction in other forums is permitted provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited in accordance with accepted academic practice distribution or reproduction is permitted which does not comply with these terms *Correspondence: Houmem Slimi, cy5ob3VtZW1AZ21haWwuY29t Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher 94% of researchers rate our articles as excellent or goodLearn more about the work of our research integrity team to safeguard the quality of each article we publish (adsbygoogle=window.adsbygoogle||[]).push({}) The look featured a pearl-white slip dress made of silk adorned with pearlescent-effect rectangular sequin embroidery and Swarovski crystals The bodice had a crisscross design that created a deep neckline flowing skirt added elegance and movement to the silhouette The look is completed by an archival marabou stole in powder pink Your invitation has been successfully sent Please search for a friend to connect first The most important transits of the month and lucky days for all signs The planet of love returns in the sign of impulsivity: what could possibly go wrong? 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You still have time to discover the Magical Women capsule Basic yes, versatile too: how-to guide for spring With a week-long takeover and many surprises in the heart of the Campania capital Boldness, sustainability and quality... all in the shades of spring We met the adidas athlete at their training center in Livorno There is one truth that burns under the skin: burnout has a gender, and too often it is female To relax in the world of pixels A phenomenon that shows no sign of stopping Portrait of a perfume alchemist who composes the invisible with rigour and creativity And sports has once again become the ground for a broader clash From the shots of Norman Parkinson and the great Magnum photographers to Giorgio Armani's Haute Couture creations You read correctly The actress is among the stars of the new series from the authors of Gilmore Girls and The Fantastic Mrs Maisel Despite the flaws, the film with Nicola Maupas is a lifesaver Bianca Balti is currently facing ovarian cancer a battle she disclosed on Instagram about a month and a half ago I went to the emergency room and discovered that my lower abdominal pain was stage 3 ovarian cancer," she wrote "It’s been a week full of tears but also love for the people I love (my daughters at the top of that list) and for everyone who needs strength: I’m lending you some of mine in anticipation of the effects of treatment without fear of losing her job or going bankrupt over medical expenses Some even questioned the usefulness or potential irritation of such posts ma lo è anche chi sta rannicchiato sul divano per giorni Va bene tutto quello che vi sentite di fare in quella situazione è solo una merda da qualunque lato guardi Another perspective is provided by breast specialist Alberta Ferrari a psycho-oncologist expert in BRCA”: “The so-called Jolie effect led to a notable increase in genetic counseling among women heightening awareness about genetic risks.. and surely was well-informed about the risks and limits of ovarian monitoring from a medical standpoint But the choice is always individual and not easy to make.” She concluded: “Enough with these headlines that instill guilt and oversimplify a complex issue upon learning of a pathogenic mutation in their families we can only say: thank you for sharing and yes But let’s respect those who are in treatment...” Doctors’ stance is clear, and many hope to see a “Bianca Balti effect” in Italy, similar to the one inspired by Angelina Jolie in 2013 The actress announced to the world that she had undergone a double mastectomy to reduce her risk of developing breast cancer Her decision was motivated by the loss of her mother and aunts to this disease and by her positive result for the BRCA1 gene Jolie’s statements created a public stir but also raised awareness not only opted for preventive mastectomies but also underwent genetic tests to check for “high-risk” genes Bianca Balti in December 2022 opted for a preventive mastectomy with the intention of eventually removing her ovaries as well Get access to exclusive contents and keep yourself updated Get access to exclusive contents and keep yourself updated Select the topics in which you are interested: Every month a newsletter to receive updates from our creative media agency So you don't miss the chance to attend nss world events Every month the latest news from the French vertical of nss Knee osteoarthritis (KOA) is a major health issue affecting millions worldwide. This study employs machine learning algorithms to analyze human gait using kinematic data, aiming to enhance the diagnosis and detection of KOA. By adopting this approach, we contribute to the development of an effective diagnostic methods for KOA, a prevalent joint condition. Our approach demonstrates significant improvements in classification accuracy, highlighting its potential for early and precise KOA detection. The model achieves a high classification accuracy, indicating its effectiveness in distinguishing KOA-related gait patterns. Furthermore, a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method, suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows. Volume 4 - 2024 | https://doi.org/10.3389/frsip.2024.1479244 This article is part of the Research TopicSmart Biomedical Signal Analysis with Machine IntelligenceView all 6 articles Introduction: Knee osteoarthritis (KOA) is a major health issue affecting millions worldwide This study employs machine learning algorithms to analyze human gait using kinematic data aiming to enhance the diagnosis and detection of KOA we contribute to the development of an effective diagnostic methods for KOA Methods: The methodology is structured around several critical steps to optimize the model’s performance These steps include extracting kinematic features from video data to capture essential gait dynamics applying data filtering and reduction techniques to remove noise and enhance data quality and calculating key gait parameters to boost the model’s predictive power The machine learning model trains on these refined features validates through cross-validation for robust performance assessment and tests on unseen data to ensure generalizability Results: Our approach demonstrates significant improvements in classification accuracy highlighting its potential for early and precise KOA detection The model achieves a high classification accuracy indicating its effectiveness in distinguishing KOA-related gait patterns a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows Gait analysis has gained popularity as an effective tool for assessing the functional consequences of knee osteoarthritis (KOA) in daily life and potential alterations in walking patterns Technological advancements have revolutionized the field of gait analysis enabling more precise and efficient methods for assessing human movement often require complex setups that include PCs and the expertise of trained professionals to operate sophisticated software they demand a well-equipped environment and considerable technical resources limiting their accessibility for widespread use In contrast, our research focuses on a simplified approach to gait classification by utilizing a reduced set of parameters extracted from basic RGB videos (Balti et al., 2024) This innovative method significantly decreases the reliance on specialized hardware and expert manipulation With the rise of human pose extraction technologies our approach facilitates the implementation of gait analysis in real-world scenarios bridging the gap between advanced research and practical application Several researchers have been working on markerless systems and kinematics to diagnose human gait and identify diseases (Liang et al., 2022). For instance, Huang et al. (2024) aimed to compare kinematic and joint moment calculations of the lower limbs during gait using both a markerless motion system and a marker-based system Sixteen healthy participants were enlisted and their lower limb kinematics were recorded simultaneously at 120 Hz by both systems while a force platform captured ground reaction forces at 1,200 Hz The data were processed in visual3D for inverse dynamics analysis revealing that the least variation in joint center position occurred at the ankle in the posterior and anterior directions with a mean absolute deviation of 0.74 cm Camera calibration via Direct Linear Transformation (DLT) allows for the precise mapping of 3D spatial coordinates onto the 2D image plane facilitating accurate 3D scene reconstruction from 2D inputs Building upon these advancements, Liang et al. (2022) focused on quantifying kinematic gait in elderly individuals to assess their health They introduced a 3D markerless pose estimation system based on OpenPose and 3DPoseNet algorithms which effectively addresses the limitations of traditional optical sensor methods thirty participants completed walking tasks and sample entropy was employed to evaluate the dynamic irregularity of gait parameters Expanding the scope of markerless pose estimation, Hu et al. (2024) proposed a new framework utilizing smartphone monocular videos This approach offers a simpler and more cost-effective alternative to traditional motion capture techniques addressing the limitations often associated with single-view technology The framework was tested with 15 healthy adults and 12 patients with musculoskeletal disorders and center-of-mass velocity while comparing results to the VICON gold standard system Finally, the study by Dong et al. (2023) explores the application of quantum machine learning to enhance knee osteoarthritis classification The authors introduce an improved hybrid quantum convolutional neural network (HQCNN) model which was initially trained on a brain tumor MRI dataset Utilizing a quantum classical transfer learning (QCTL) approach they fine-tune the model and extract features based on previously trained weights Testing the HQCNN structure on the knee osteoarthritis dataset (OAI) they achieved a classification accuracy of 98.36% Gait kinematics refers to the study of human walking patterns and movements encompassing the detailed analysis of joint angles and overall body posture during the gait cycle As a specialized field within biomechanics gait kinematics emphasizes the quantitative assessment of how individuals walk providing critical insights into the mechanics of human locomotion This analysis is particularly significant in understanding the impact of osteoarthritis on mobility and functional performance One of the key components of gait kinematics is joint angle measurement which involves quantifying the angles at which various joints move during walking alterations in joint angles are commonly observed especially in weight-bearing joints such as the knees and hips These changes can indicate compensatory strategies that individuals adopt to manage pain and maintain mobility Osteoarthritis often leads to modifications in these parameters as individuals adjust their walking patterns to minimize discomfort a person with osteoarthritis may shorten their stride or alter their step width to relieve pressure on affected joints These adaptations are essential to monitor as they can provide valuable information about the severity of the condition and the effectiveness of therapeutic interventions Joint loading patterns are another crucial aspect of gait analysis This component examines how weight is distributed across joints during movement revealing the potential for altered loading in individuals with osteoarthritis affected individuals may unconsciously shift their weight away from the painful joint leading to imbalances that can further exacerbate mobility issues Analyzing these loading patterns can help healthcare professionals develop tailored rehabilitation strategies The assessment of asymmetry in gait is also vital as osteoarthritis can lead to noticeable differences in movement between legs if one knee is more affected by osteoarthritis the individual may exhibit a distinct asymmetry in their walking pattern which can contribute to increased strain on other joints Recognizing these asymmetries is essential for implementing effective treatment plans that address the underlying issues which involve measuring time intervals and distances during walking further enrich the understanding of gait kinematics Changes in these parameters may occur as individuals with osteoarthritis adapt their movements to reduce pain clinicians can gain insight into how the disease affects mobility and identify appropriate interventions pain assessment plays a crucial role in gait analysis By combining objective gait measurements with self-reported pain levels clinicians can develop a comprehensive understanding of how osteoarthritis influences an individual’s walking pattern This multifaceted approach enhances the ability to address the patient’s needs effectively Longitudinal monitoring of gait kinematics is invaluable for tracking changes over time This method allows for the assessment of osteoarthritis progression and the evaluation of the effectiveness of various interventions healthcare providers can make informed decisions regarding treatment adjustments gait kinematics offers objective assessment tools that provide quantifiable data on how osteoarthritis impacts mobility This objective measurement can complement subjective evaluations leading to better treatment planning and patient outcomes Several medical research laboratories have developed databases to facilitate research on conditions like knee osteoarthritis (KOA) and Parkinson’s disease (PD). One such dataset is the KOA-PD-NM (Kour et al., 2020), which we have utilized in our study (Figure 1) This dataset accounts for key variables such as age providing a robust foundation for analyzing both normal (NM) and abnormal (KOA Its unique contribution lies in enabling the comprehensive evaluation of not only lower body movements but also upper body dynamics Figure 1. Examples of videos on knee osteoarthritis from Kour et al. (2020) we leverage the KOA-PD-NM dataset to compare gait deviations between patients and healthy individuals we aim to enhance the understanding of disease progression and contribute to the development of more refined diagnostic techniques and strategies The KOA-PD-NM Gait Video Dataset (Kour et al., 2020) consists of 96 subjects: 50 with KOA, 16 with PD, and 30 NM subjects (Table 1) Each individual is represented by two sequences left-to-right and right-to-left captured in the frontal and sagittal planes recorded using a NIKON DSLR 5,300 camera positioned 8 m away from a walking mat in a clinical setting While the original dataset involved the use of six red passive reflective markers on the body joints our study takes a different approach by detecting gait abnormalities without relying on these markers aiming for a more accessible and markerless diagnostic method Table 1. Database composition (Kour et al., 2020) showcasing their importance in accurately analyzing and classifying gait patterns Examples of marker-less body point extraction Comprehensive flowchart of the kinematic gait classification methodology using machine learning techniques Throughout each phase of the gait cycle, various kinematic parameters are used to represent and analyze human walking patterns (Rodrigues et al., 2020). Machine learning techniques are applied to process and analyze the data, offering a precise method for assessing human gait. The specific kinematic features selected for this project are detailed in Table 2 Kinematic gait parameters used to classify individuals with knee osteoarthritis (KOA) Below is a full explanation of the calculations and key parameters accompanied by illustrative figures that depict the precise positioning of angles These visualisations include example curves that effectively demonstrate the dynamic patterns of gait providing a deeper insight into the biomechanical characteristics essential to the analysis To measure the angle (Equation 1), we used a function that calculates the angle between three reference points and computes the angle between two lines. The first point is considered the starting point of the first line, the second point is regarded as the endpoint of the first line and the starting point of the second line, and the third point is considered the endpoint of the second line (Figure 4) Calculation of the angle between three points y3) represent the other two points defining the vectors: The final angle is determined by averaging the values of both the flexion and extension angles over a specific number of gait cycles ensuring that the resulting angle is an aggregate measurement The angle is the mean value of the N measurement angle flexion and extension φ=φ1,φ2,φ3,…φN Where φ¯ (Equation 2) is the mean set of φ: Knee angle measurement during gaite cycles7 Ankle dorsiflexion and plantar flexion are key movements at the ankle joint, essential for various activities like walking, running, and jumping. Dorsiflexion involves pulling the toes upward toward the shin, while plantar flexion refers to pointing the toes downward. These movements play a vital role in gait dynamics, enabling proper foot clearance and propulsion during the gait cycle (Figure 7) Ankle dorsiflexion and plantar flexion angle measurement during gaite cycles The typical range of motion for ankle dorsiflexion is approximately 10–20° while plantar flexion usually falls within 30–50° these ranges can vary significantly based on factors such as age Reduced range of motion in either direction may indicate underlying musculoskeletal issues often seen in conditions like osteoarthritis or after injury Understanding these movements and their variability is crucial for assessing gait abnormalities and developing targeted therapeutic interventions The step length (Equation 3) was quantified along the horizontal axis of the walking track by measuring the distance from the right heel to the left heel when both feet were in contact with the ground (Figure 8) Step length measurement during gaite cycles The step length exhibited by the maximum value in the graph was calculated by the average of the number of reputations Where: Xleft¯ , Xright¯ is the mean set of X left step lengths (Equation 4) and X right step lengths (Equation 5) The step height parameter refers to the maximum elevation of the foot relative to the ground during walking (Figure 9). This height is computed as the difference between the maximum Ymax and minimum Ymin coordinates during a complete gait cycle (Equation 6): Step height measurements during gaite cycles Y¯ is the mean set of Y (Equation 7): Gait symmetry refers to the degree of similarity between the movements of the left and right limbs during activities like walking or running as it quantifies how closely the movement patterns of one side of the body mirror those of the opposite side This concept is particularly important for assessing balance and coordination as well as identifying potential movement impairments various kinematic parameters such as joint angles Discrepancies in these parameters between the left and right sides can reveal underlying imbalances or irregularities asymmetry in joint angles or step length may indicate musculoskeletal disorders or compensatory patterns due to pain or injury Accurate measurement of gait symmetry helps clinicians and researchers identify movement inefficiencies and tailor rehabilitation programs to restore normal and balanced gait patterns The assessment of gait symmetry involves the utilization of various comparative techniques. In our study, we chose to use Pearson correlation coefficient noted “r” (Equation 8) The Pearson correlation coefficient is often denoted as r and is widely used in statistics and data analysis to explore relationships between variables The Pearson correlation coefficient ranges from −1 to 1 1 indicates that two random variables are perfectly positively correlated −1 indicates that two random variables are perfectly negatively correlated and 0 indicates that two random variables are not correlated The formula for calculating the Pearson correlation coefficient between two variables Xi and Yi are the individual data points for variables X and Y X¯ and Y¯ are the mean values of variables X and Y Figure 10 illustrates the correlation between the left and right knee angles of a normal individual and a person diagnosed with knee osteoarthritis (KOA) The blue points represent the Pearson correlation coefficients for the normal group (NM) indicating a healthy and symmetrical movement pattern the red points represent the Pearson correlation coefficients for the KOA_SV group which corresponds to individuals in the severe stage of knee osteoarthritis This visual comparison highlights the differences in knee angle relationships between the two groups emphasizing how the severity of osteoarthritis impacts gait symmetry and joint function Correlations of left and right knee angles between a normal person and a person with knee osteoarthritis We conducted our experiments utilizing the gait dataset to analyze the severity of knee osteoarthritis and Parkinson’s disease (Bishop et al., 2016) This comprehensive dataset encompasses gait data from both healthy individuals and those diagnosed with knee osteoarthritis (KOA) providing a valuable resource for our analysis we specifically focused on a subset of the dataset that included 30 video recordings from normal Each subject was recorded performing two sequences of gait one from left to right and the other from right to left all captured in the frontal and the sagittal plane the dataset features 50 video files that document various stages of knee osteoarthritis categorized into three distinct severity levels: early This structured approach allows for a nuanced understanding of how gait is affected across different stages of KOA To classify human gait patterns effectively we employed three different machine learning algorithms: logistic regression The primary aim of this classification was to differentiate between healthy individuals and those with varying grades of knee osteoarthritis based on their gait characteristics Following the training of our models, the dataset was divided into training and validation sets, with 20% reserved for testing the algorithms’ performance. To enhance the robustness of our findings, we utilized cross-validation techniques alongside the scikit-learn library for our analysis. The results of this evaluation, including performance metrics for each model, are summarized in Table 3 offering insights into the efficacy of each classification approach in distinguishing between the two groups Precision and sensibility result in classification of knee osteorathritis and normal subjects using logistic regression Following a comprehensive analysis of the training process using this limited dataset we found that the random forest model exhibited a remarkable accuracy of 96.9% significantly outperforming the other algorithms the logistic regression model achieved an accuracy of 94% while the support vector machine (SVM) model lagged behind at 88% These results highlight the random forest model’s robustness and effectiveness in classifying gait patterns associated with knee osteoarthritis there is a clear opportunity for improvement with the other models By expanding the dataset to include a greater variety of samples and potentially more diverse gait patterns we anticipate that both the logistic regression and SVM models will demonstrate enhanced performance This expansion could lead to more reliable classifications and a better understanding of the subtle gait variations present in individuals with varying degrees of knee osteoarthritis Figure 11 highlights the performance of the SVM model through its learning curve The learning curve indicates a steady increase in accuracy as training progresses This suggests that the model’s performance could further improve with additional training data The validation curve shows optimal performance at a C value of 10^1 striking a balance between underfitting and overfitting The confusion matrix reveals the model’s high accuracy in predicting healthy individuals it struggles slightly with predicting unhealthy cases incorrectly classifying 3 out of 12 as healthy This indicates room for refinement in handling complex cases Figure 12 illustrates the learning curve and confusion matrix for the Random Forest classifier The learning curve demonstrates a consistent improvement in accuracy as the training set size increases stabilizing at 94% after reaching 80 training examples The validation curve reveals that accuracy initially improves as the regularization parameter C increases further increases cause a decline in performance The optimal C value is around 10^−1 The confusion matrix showcases the model’s effectiveness with only 2 false negatives and no false positives highlighting its strong capacity to distinguish between healthy and unhealthy individuals though additional fine-tuning could potentially improve its performance even further Figure 13 presents the performance of the Random Forest model via its learning curve The learning curve shows that accuracy steadily improves stabilizing after 60 training examples and reaching 86% The validation curve demonstrates that high accuracy is achieved quickly with a small number of estimators (trees) with diminishing returns beyond that point The confusion matrix confirms the model’s strong performance This indicates a highly accurate model with only one misclassification emphasizing its robustness in both identifying healthy and unhealthy cases The Random Forest model likely outperformed both Logistic Regression and SVM due to its ability to capture non-linear relationships and complex interactions between features This capability allows it to excel even with smaller datasets by generating diverse models that generalize better Logistic Regression and SVM are more prone to overfitting or failing to recognize complex patterns without sufficient data The Random Forest’s inherent capacity to handle feature complexity and variability makes it a more reliable choice in this scenario In this experiment, the primary objective was to classify individuals based on the severity of knee osteoarthritis (KOA), with a focus on evaluating the performance of two distinct classification methods: Support Vector Machine (SVMs) and Logistic Regression. The aim was to determine how effectively these models could differentiate between varying levels of KOA severity. A comprehensive comparison (Table 4) was conducted by analyzing key performance metrics we sought to gauge the models’ ability not only to correctly categorize individuals but also to minimize false positives and false negatives thus providing a more precise evaluation of their reliability in identifying KOA severity stages This comparison is essential for identifying the most suitable model for clinical applications where accurate classification is critical for effective diagnosis and treatment planning Accuracy and precision of classifying knee osteoarthritis severity using SVM and logistic regression the accuracy achieved in this experiment may not be sufficient One limitation is the relatively small dataset used likely contributed to overfitting The limited sample size likely contributed to the reduced performance of both classification methods their approach necessitates a controlled lab environment and requires considerable space and time to accurately capture data limiting its practicality in real-world settings utilizes a Random Forest classifier and achieves an accuracy of 96.9% This approach not only surpasses the accuracy of previous methods but also offers the advantage of being non-invasive and more adaptable to diverse environments further improvements in performance can be achieved with access to larger datasets for training and refinement Accuracy comparison between the proposed method and other state-of-the-art methods Gait classification in patients with knee osteoarthritis (KOA) is a critical area of research with the potential to improve diagnosis Analyzing gait patterns provides valuable insights into the biomechanical changes associated with KOA enabling more personalized and effective interventions gait classification in KOA remains complex with ongoing advancements in classification systems and technologies aimed at better understanding the impact of KOA on gait Personalized treatment approaches based on gait classification are also emerging While this study presents an effective method for KOA classification The relatively small dataset impacts both the accuracy and generalizability of the machine learning model Expanding the dataset with more diverse samples is crucial for improving the model’s robustness relying solely on RGB videos for gait analysis limits the data scope compared to more advanced systems that incorporate sensors like IMUs Future research could benefit from integrating multi-modal data to enhance the precision of KOA classification and clinical validation is necessary to ensure real-world applicability this research demonstrates the potential of markerless vision-based gait classification for KOA assessment using machine learning the method provides valuable insights that aid in diagnosis future work should focus on expanding the dataset and refining the algorithm to improve accuracy and clinical relevance this approach shows promise as a non-invasive with the potential to improve personalized care for patients The original contributions presented in the study are included in the article/supplementary material further inquiries can be directed to the corresponding author Human gait analysis using gait energy image Google Scholar Enhanced fingerprint classification through modified PCA with SVD and invariant moments PubMed Abstract | CrossRef Full Text | Google Scholar “Gait analysis and detection of human pose diseases,” in 2022 8th International Conference on Control Decision and Information Technologies (CoDIT) CrossRef Full Text | Google Scholar Asymmetries of the lower limb: the calculation conundrum in strength training and conditioning CrossRef Full Text | Google Scholar “Realtime multi-person 2d pose estimation using part affinity fields,” in Proceedings of the IEEE conference on computer vision and pattern recognition CrossRef Full Text | Google Scholar Classification of knee osteoarthritis based on quantum-toclassical transfer learning CrossRef Full Text | Google Scholar Patients with knee osteoarthritis demonstrate improved gait pattern and reduced pain following a non-invasive biomechanical therapy: a prospective multi-centre study on Singaporean population PubMed Abstract | CrossRef Full Text | Google Scholar Knee osteoarthritis: current status and research progress in treatment (Review) PubMed Abstract | CrossRef Full Text | Google Scholar Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video PubMed Abstract | CrossRef Full Text | Google Scholar Comparison of kinematics and joint moments calculations for lower limbs during gait using markerless and marker-based motion capture PubMed Abstract | CrossRef Full Text | Google Scholar Human pose estimation using mediapipe pose and optimization method based on a humanoid model CrossRef Full Text | Google Scholar Kour, N., Gupta, S., and Arora, S. (2020). Gait dataset for knee osteoarthritis and Parkinson’s disease analysis with severity levels. [Online]. Available at: https://zenodo.org/records/3369237 Google Scholar Gait analysis of bilateral knee osteoarthritis and its correlation with Western Ontario and McMaster University Osteoarthritis Index assessment PubMed Abstract | CrossRef Full Text | Google Scholar The reliability and validity of gait analysis system using 3D markerless pose estimation algorithms PubMed Abstract | CrossRef Full Text | Google Scholar A review of techniques on gait-based person re-identification CrossRef Full Text | Google Scholar Human gait assessment using a 3D marker-less multimodal motion capture system CrossRef Full Text | Google Scholar Review-emerging portable technologies for gait analysis in neurological disorders PubMed Abstract | CrossRef Full Text | Google Scholar “Deep high-resolution representation learning for human pose estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition CrossRef Full Text | Google Scholar and affordable human gait analysis system using bottom-up pose estimation with a smartphone camera CrossRef Full Text | Google Scholar Novel method of classification in knee osteoarthritis: machine learning application versus logistic regression model CrossRef Full Text | Google Scholar Ben Khelifa MM and Sayadi M (2024) Markerless vision-based knee osteoarthritis classification using machine learning and gait videos Received: 11 August 2024; Accepted: 31 October 2024;Published: 21 November 2024 Copyright © 2024 Ben Hassine, Balti, Abid, Ben Khelifa and Sayadi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use *Correspondence: Ala Balti, YWxhYS5iYWx0aUBlbmljYXIudWNhci50bg== Every Oscars celebration throws up an unexpected talking point The Italian supermodel and cancer patient attended the Vanity Fair Oscars party in Beverly Hills wearing a cream and sheer Fendi couture gown and in place of her usual luscious and long brown hair one hailed by fashion designers such as Stefano Gabbana and fellow models such as Irina Shayk Balti has had stage 3C ovarian cancer — where the cancer has spread beyond the pelvis — and been undergoing chemotherapy Balti walks the runway at the Blumarine show during Milan Fashion Week in 2017PIETRO D’APRANO/GETTY“I felt like Cinderella because I had to leave [the party] by midnight,” the 40-year-old says a band of Baltis—an ethnic-linguistic group scattered through Pakistan-administered Gilgit-Baltitstan and the India-administered territory of Ladakh—convenes to create music that sonically travels across borders even when the people making it physically can’t the Balti Group’s latest labour of love is titled ‘Yusha Inna,’ a song on star-crossed lovers Laila and Majnu Sadiq Ali Ashoor and other featured artists over a gorgeous montage of vignettes from their homeland It’s about the deep connection to one’s roots and the ache of being apart from them,” explains Zahra Banoo The song resonates not only as a love story but as a timeless reflection on identity and the emotional pull of one’s homeland which the Balti folk—separated from their majority in Pakistan—must endure in Ladakh Banoo chalks her role in this project up to fate explaining how she met the group by accident at a singing competition seven years ago “Abbas sir saw me perform and invited me to join their troupe which was around 60 artists and has grown by 20 since I joined,” she says Banoo’s incredible voice coupled with Sadiq Ali Ashoor’s emotive performance breathes new life into an old folk song that has long been cherished at Balti weddings and celebrations The official music video for ‘Yusha Inna’ is a love letter to Kargil’s culture and terrain. Shot entirely in Kargil, it captures the Balti Group travelling by local bus through the region, dressed in traditional topis and gonchas, overcoat-dresses secured by kamarbandhs Kargil’s everyday scenes—villagers smiling landscapes stretching on endlessly—become a canvas for the song’s heartfelt lyrics It’s a way of showing our culture to the world—our traditions our kindness and our connection to the land.” At the heart of this project is Renzu Music, a Srinagar-based record label that has been redefining how independent music from Kashmir and its surrounding regions is heard. Founded by Danish Renzu, the label has collaborated with some of the biggest names in Indian music like Sonu Nigam and Shankar Mahadevan “Not many people know about the Baltis,” Danish shares “When I saw the Balti Group perform at The Himalayan Film Festival in 2023 Their raw talent and cultural depth needed a platform “This Balti ghazal has been taken out of its cage and set free to fly around the world for the first time.” This India-Pakistan couple set sail on a yacht with a Dil Dhadakne Do-inspired baraat for their wedding Vogue’s India-Pakistan blogger Suhair Khan Everyone wrote off her deaf daughter. So Pakistan’s Aniqa Bano built a whole school for the deaf This is 9 of the Super Liga Championship Group Predicted lineups are available for the match a few days in advance while the actual lineup will be available about an hour ahead of the match The current head to head record for the teams are FC Balti 4 win(s) Have scored 5 goals in their last 5 matches Caio Ferreira is the competition's top scorer (10) Have scored 12 goals in their last 5 matches Have kept the most clean sheets in the competition (12) FC Sheriff haven't lost to FC Balti in their last 5 meetings (4W FC Balti is playing home against FC Sheriff on Sat Sonam Kapoor’s Mumbai home is truly something special far beyond the usual glamorous celebrity digs gave Architectural Digest India an inside look at their stunning flat The space is a beautiful blend of Indian influences with a modern twist showcasing their unique style and attention to detail One of the standout features that caught people’s eye was a humble ‘steel balti’ (bucket) turned vase A fan posted a clip of Sonam talking about this particular item in her bar area where it sat among elegant glass vases on a marble and wooden table This seemingly simple object sparked a bit of a buzz online Some fans thought it was a rare affordable piece in an otherwise lavish home while others pointed out it was actually a silver bucket The conversation around the ‘balti’ turned playful with people joking about this everyday item amidst the high-end decor It’s a great example of how even the simplest things can draw attention when they're part of a thoughtfully designed space Sonam Kapoor’s home isn’t just about luxury; it’s about blending high-end style with personal touches that reflect her cultural heritage This mix of grandeur and relatable elements offers a refreshing perspective on celebrity living highlighting how thoughtful details can make a home feel truly special SEE ALSO: Didn't Greet Ajay Devgn..’ Vijay Raaz Speaks Out About Son Of Sardar 2 Controversy Amid Advance Dispute NOT only just one, but two kabalens are eagerly awaiting Converge big man Justine Baltazar as he plays his first PBA game against the Magnolia Hotshots. The season’s top rookie pick expects no red carpet welcome from Calvin Abueva and Ian Sangalang as the FiberXers and Hotshots battle each other out in one of the special presentations by the league when it celebrates its 50th founding anniversary on Wednesday at the Rizal Memorial Coliseum tussle marks the first time Baltazar will go up against two of the best bigs to come out of Pampanga which had been home to some of the country’s finest basketball players When the two teams met in the last Commissioner’s Cup in December the 6-foot-8 De La Salle product was in the middle of MPBL Finals playing for the Pampanga Giant Lanters against the Quezon Huskers on the heroics of Alec Stockton down the stretch capping the team’s comeback from a 20-point deficit Baltazar wouldn’t suit up for Converge until 11 days later on Dec “Last conference hindi ko sila nakalaban,” recalled the 28-year-old native of Mabalacat Pampanga) are all proteges of Pampanga Governor Dennis ‘Delta’ Pineda Baltazar has so much respect to the two Magnolia veterans “Siguro magiging ready lang ako kasi yun ang mga kuya ko na mga Kapampangan Magiging ready lang ako sa mga kuya ko,” he added Baltazar warmed up for the highly-anticipated showdown by grabbing 16 rebounds and finishing with nine points in the FiberXers’ 92-83 win over Phoenix Sunday night at the Ninoy Aquino Stadium It was the first win of the conference for Converge Get more of the latest sports news & updates on SPIN.ph Spin.ph has been granted the NPC Seal of Registration in recognition of the successful registration of its DPO and DPS We use cookies to ensure you get the best experience on Spin.ph. 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Find out more here JORDAN Heading and Justine Baltazar broke out in their first Christmas Day game in the PBA as Converge won its fourth straight game by beating Meralco on Wednesday in the Commissioner’s Cup at the Smart-Araneta Coliseum Heading and Baltazar led the charge of the locals as the FiberXers improved their record to 6-2 while dealing the Bolts their second straight loss in five matches which also got 24 points and 19 rebounds from import Cheick Diallo Converge coach Franco Atienza was glad that the FiberXers were able to lead throughout the match while also taking advantage of the Bolts’ injury bug with Akil Mitchell and Allein Maliksi just coming back to their team The FiberXers led by as many as 20 points in the contest This is a championship caliber team and they are plagued with injuries,” said Atienza they are not in their best form although they competed and na-challenge din kami Mitchell had 29 points and 18 rebounds in his comeback game after missing the last two matches of the Bolts due to a broken nose but only Cliff Hodge was the only remaining Bolt to breach double-digits with 10 our people are agriculturists and livestock farmers; they have also been employed by security forces such as the Indian Army our village became one of the most popular tourist areas in Leh Our people hadn’t planned for it; there was no existing infrastructure They just started constructing cement and concrete guest houses and buildings for the visitors These new buildings replaced the local architecture and was perfect for Leh’s climate because it promoted insulation and kept the houses warm during winters and cool during summers The spike in tourism brought an economic boom we became a tourism-focused village and many people gave up on agriculture and other traditional activities Since we no longer produce much of the food that we consume Non-biodegradable plastic packets can be found everywhere in the village; these pollute our water sources and have an overall negative impact on our climate The increase in pollution and unsustainable construction has exacerbated climate change in the region Even the few farmers who try to continue farming are discouraged because they can no longer predict the weather we depended on the melting snow in the valley to irrigate our crops during the sowing season in March–April But now sudden surges in temperature are causing the snow to melt earlier than usual and there’s no water left when the crops need it Our own council will manage Turtuk’s development in a sustainable manner It will also support our cultural conservation efforts such as the one for the revival of the Yige script Ghulam Mehdi Shah is a climate activist and member of Leh Apex Body Know more: Learn why unemployment has become worse in Ladakh since 2019 Do more: Connect with the author at ghulammehdi54@gmail.com to learn more about and support his work If you like what you're reading and find value in our articles India Development Review is published by the Forum for Knowledge and Social Impact a not-for-profit company registered under Section 8 of the Company Act your one-stop destination for the latest sports articles and news sports articles We are a sports news portal that covers a wide range of sports After scripting a historic 295-run victory at the Optus Stadium in Perth who scored a magnificent century to give India a 1-0 lead in the five-Test series against Australia taking a leisurely stroll on St Georges Terrace He paused briefly in front of ‘Balti’ restaurant an Indian restaurant located at the heart of Perth has become a cherished spot for the Indian cricket team offering a taste of home amid their travels The restaurant has been particularly busy this time with owner Ashwani Notani hosting around 40 Indian players including members of both the main squad and the India A team who were in Perth preparing for the opening Test Even after the India A players returned home the tradition of visiting ‘Balti’ for authentic Indian meals continued through the Test match and into the supposed fifth day of the game on Tuesday Abhimanyu Easwaran and Dhruv Jurel have been frequent visitors ‘Balti’ is not just about the food it’s also about the privacy it affords its guests the restaurant staff ensure they can dine undisturbed They started coming in from the very first day and it continued till the very last day that was November 26 who had toured here before already knew about us so the word was already within the team,” Notani “Ravichandran Ashwin was the first one who came here this time and from there the likes of Siraj For the Latest Sports News: Click Here Notani added: “I know Gautam Gambhir from his playing days And it’s always special to host Gambhir here We cook it in Delhi style with desi tadka and masalas so they feel less homesick.” With modern cricketers focusing more on fitness Notani noted: “It has happened for the first time that the demand for jeera rice is very high with desi ghee among the players the players didn’t use to emphasise a lot on their diet but now they are very precise in what and how much they eat We don’t pre-cook and have as many as six chefs on busy days The best thing about Indians is hospitality and we serve people here as we would host them at our home.” The walls of ‘Balti’ are adorned with photos of cricket legends like Tendulkar It’s something they’ve requested prides himself on using local Australian products to craft authentic Indian dishes “I was really wanting Jasprit Bumrah to come Virat Kohli has become really strict with his diet but earlier he used to love our tandoori chickens and aloo parathas they might get troubled by fans on the street,” the Delhi-born restaurateur said As the Indian team moves on to Canberra and beyond they will carry with them the comforting taste of India from ‘Balti’ Also Read: Fans’ focus should be on an Indian victory in Adelaide, not the captaincy NORTHPORT spoiled the debut of top rookie pick Justine Baltazar behind a 108-101 win over Converge to keep the top spot in the PBA Commissioner’s Cup Joshua Munzon nailed back-to-back three pointers to break a tight 97-97 game and give the Batang Pier a six-point lead it would never relinquish on the way to the thrilling win The Batang Pier remained unbeaten with a 5-0 record its finest start in franchise history and equaling the team’s longest winning streak Coach Bonnie Tan said the Batang Pier was aware the 6-foot-8 Baltazar 1 overall pick in the last draft and two-time MVP in MPBL will be playing his first PBA game against them “We just discussed na huwag naming ibibigay sa isang rookie (in his first game) yung magandang ginagawa natin sa first four games,” said Tan of Northport’s pre-game huddle “At least nag step up yung players natin to get that fifth win Kumbaga yung run that was done ny team ay masisira lang ng isang rookie player So na-challenge siguro sila sa ganun and na-realize ng mga players na masasayang nga yung 4-0 start nila.” while scoring 13 of his points in the final quarter to take away the spotlight from Baltazar coming off a back-to-back championship-MVP stint with the Pampanga Giant Lanterns in the MPBL suited up for 20 minutes and finished with five points and three assists in his first outing in the league Despite the center stage falling on Baltazar another rookie in Jordan Heading carried the fight for Converge which led by as many as 13 points early in the second period Heading had 10 of his 30 points in the fourth quarter on 5-of-8 shooting from three-point range while Alec Stockton added 15 (3-of-4 from beyond the arc) including the three that tied the game for the last time at 97 with 2:55 to go in the game Munzon then hit two from long range and Kadeem Jack had a lucky bounce from the same spot for a 9-2 exchange that gave the Batang Pier a 196-99 lead with 1:26 remaining Jack had 30 points as he and Munzon became the first Northport teammates to finish with 30 points in the same game since Arvin Tolentino had 35 and Venky Jois with 30 in the team’s 115-103 win over Rain or Shine in last season’s Commissioner’s Cup Converge fell to an even 2-2 record as import Cheick Diallo got into foul trouble and only contributed 13 points and nine rebounds Quarterscores: 24-33; 54-57; 75-76; 108-101 Harborne Indian Kitchen staff prepare for the 'revolution'(Image: Harborne Indian Kitchen)A Birmingham Indian restaurant that's been closed for four months has reopened and is planning a 'balti revolution' in one of the city's prominent foodie neighbourhoods Harborne Indian Kitchen is the new name of the former Umami restaurant a neighbourhood spot which earned great reviews over the last decade for its 'excellent' service and 'exceptional' food The Lordswood Road restaurant has rebranded the venue overhauling the space and introducing a 'bring your own' drinks rule Read more: I tried the 'ugly' off-menu Costa Coffee that looks like a crime scene and got it all wrong Read more: Ozzy Osbourne's touching gesture to lifelong fan that 'lost' Birmingham charity auction though a Kashmiri chef has been brought in to specialise in baltis and help diversify the menu at the Harborne hotspot Abdul told BirminghamLive: "When we were Umami we served North Indian food but now it's a spectrum of North Indian "The reason we did that is because balti has started to lose its identity in Birmingham It needs to be revamped and rejuvenated because it's a tradition "Being in Harborne, an affluent area, there was nowhere better to start than here. It's a balti revolution." The restaurant served its first customers under the Harborne Indian Kitchen name on Thursday, April 25. Harborne Indian Kitchen has reopened(Image: Harborne Indian Kitchen)"We're all very excited," Abdul said ahead of opening. "We've been here ten years and the neighbourhood has a great reputation, it's like the Notting Hill of Birmingham! "But when you come in our restaurant, it's a homely feel. The neighbourhood is great and everyone's been really wonderful. "The love and affection the community showed us when we were closed told us that we should come back." Abdul said the restaurant has a Kasmiri balti chef he says is the 'best of the best', and that guests are welcome to bring their own drinks to accompany the dish in their preferred way. "We've seen the cost of living impact customers so we kept that in mind when changing to 'bring your own bottle'. "It also isn't sustainable for us, with the way business is going, to stock everybody's favourite. So we let customers bring their own drinks and we just focus on what we know we can do well, which is the food." Harborne Indian Kitchen is at 25 Lordswood Road, Harborne, B17 1RP.