Knee Osteoarthritis Severity Prediction from X-Rays Using Ensemble Learning

Knee osteoarthritis (OA) affects millions of individuals globally and is characterized by the degeneration of joint cartilage and underlying bone. This condition leads to pain, limited movement, and reduced mobility. Accurate and timely assessment of OA severity is critical for effective clinical management. In this study, we address the challenge of predicting knee OA severity using X-ray images by leveraging deep learning, particularly convolutional neural networks (CNNs). A novel ensemble model is proposed, combining the strengths of multiple architectures to improve diagnostic accuracy. Specifically, the model integrates feature extraction from EfficientNet and DenseNet, along with other ensemble techniques, achieving an impressive accuracy of 94.76%. The high performance of the ensemble model highlights its robustness in classifying OA severity and presents a reliable, precise approach for health assessment. This systematic methodology demonstrates the potential of deep learning to significantly enhance diagnostic accuracy and support informed clinical decision-making in knee osteoarthritis care.