KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
This addresses the time-intensive expert assessment problem for knee osteoarthritis diagnosis, though it is incremental as it builds on existing deep learning methods.
The study tackled automated classification of knee osteoarthritis severity from X-ray images, achieving an accuracy of 0.72 with an ensemble model called KneeXNet.
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.