Automatic Knee Osteoarthritis Severity Grading using Deep Neural Networks: Comparative Analysis of Network Architectures and Optimization Functions

Abstract

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that requires accurate assessment of its severity for effective treatment planning. In this study, we propose an automatic knee OA severity-grading system based on deep neural networks. Specifically, we explore various network architectures, including VGG-16, VGG-19, ResNet-101, EfficientNet-B7, and EfficientNet-B6, along with different optimization functions such as SGD, ADAM, Nadam, AdamW, and AdaDelta. Furthermore, we investigate two loss functions, namely, the novel ordinal loss and the cross-entropy loss. The proposed system is evaluated on a carefully curated dataset, and comprehensive experimental settings are employed to ensure reliable results. Our findings indicate that the combination of the EfficientNet-B7 network with the Nadam optimizer yields the best performance, achieving an accuracy of 70.1% in knee OA severity grading. These results demonstrate the potential of deep neural networks in automating the grading process, offering a valuable tool for clinicians and researchers in the field of knee osteoarthritis management.