KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
Journal:
arXiv
Published Date:
Dec 10, 2024
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.
