KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation

• URL: http://arxiv.org/abs/2412.07526v1
• Date: Tue, 10 Dec 2024 14:02:04 GMT
• Title: KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
• Authors: Nicharee Srikijkasemwat, Soumya Snigdha Kundu, Fuping Wu, Bartlomiej W. Papiez,
• Abstract summary: Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. This study developed an automated deep learning model to classify knee OA severity, reducing the need for expert evaluation. Our ensemble model, KneeXNet, achieved the highest accuracy of 0.72, demonstrating its potential as an automated tool for knee OA assessment.
• Score: 5.715859759904031
• License:
• Abstract: 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.

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