KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915057147838464 |
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| author | Srikijkasemwat, Nicharee Kundu, Soumya Snigdha Wu, Fuping Papiez, Bartlomiej W. |
| author_facet | Srikijkasemwat, Nicharee Kundu, Soumya Snigdha Wu, Fuping Papiez, Bartlomiej W. |
| contents | Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is commonly based on the Kellgren-Lawrence grading system, a time-intensive process. This study aimed to develop an automated deep learning model to classify knee OA severity, reducing the need for expert evaluation. First, we evaluated ten state-of-the-art deep learning models, achieving a top accuracy of 0.69 with individual models. To address class imbalance, we employed weighted sampling, improving accuracy to 0.70. We further applied Smooth-GradCAM++ to visualize decision-influencing regions, enhancing the explainability of the best-performing model. Finally, we developed ensemble models using majority voting and a shallow neural network. Our ensemble model, KneeXNet, achieved the highest accuracy of 0.72, demonstrating its potential as an automated tool for knee OA assessment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_07526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation Srikijkasemwat, Nicharee Kundu, Soumya Snigdha Wu, Fuping Papiez, Bartlomiej W. Image and Video Processing Computer Vision and Pattern Recognition Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is commonly based on the Kellgren-Lawrence grading system, a time-intensive process. This study aimed to develop an automated deep learning model to classify knee OA severity, reducing the need for expert evaluation. First, we evaluated ten state-of-the-art deep learning models, achieving a top accuracy of 0.69 with individual models. To address class imbalance, we employed weighted sampling, improving accuracy to 0.70. We further applied Smooth-GradCAM++ to visualize decision-influencing regions, enhancing the explainability of the best-performing model. Finally, we developed ensemble models using majority voting and a shallow neural network. Our ensemble model, KneeXNet, achieved the highest accuracy of 0.72, demonstrating its potential as an automated tool for knee OA assessment. |
| title | KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.07526 |