Automated Knee Osteoarthritis Severity Grading in X-ray Images Using Transfer Learning

Abstract

Knee osteoarthritis (OA) is a chronic degenerative joint disease that leads to cartilage breakdown, joint space narrowing, and osteophyte formation, significantly impacting mobility and quality of life. Early and accurate grading of OA severity is crucial for effective treatment and disease management. X-ray imaging is the most widely used diagnostic tool for knee OA, providing a cost-effective and non-invasive method to assess structural changes in the knee joint. However, manual interpretation of X-rays is subjective and prone to inter-observer variability, necessitating the development of automated AI-based diagnostic systems. This study proposes a deep learning-based approach for knee OA severity grading using transfer learning with MobileNet and ResNet models. The results indicate that ResNet significantly outperformed MobileNet, achieving 83.5% accuracy, 83.0% precision, and 83.1% recall, compared to 74.1%, 71.5%, and 71.0%, respectively, for MobileNet. Statistical analysis using ANOVA confirmed significant differences (p < 0.05) in classification performance between the models.

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