Deep Learning-Based Osteoarthritis Severity Classification from Radiographic Images (X-Ray)

Osteoarthritis (OA) is a common degenerative joint disease that has a major global health effect. For efficient treatment planning and illness management, early identification and precise OA severity assessment are essential. In this work, we use deep learning techniques to present a complete method for classifying the severity of osteoarthritis (OA) from radio-graphic images. Preprocessing the dataset, optimizing pre-trained models, utilizing ensemble learning strategies, and assessing model performance are all part of our methodology. The X-ray images from different sources that are preprocessed to improve quality and uniformity make up the knee OA dataset utilized in this study. In order to tailor various pre-trained deep learning models—DenseNet, EfficientNet, InceptionNet, MobileNet, and ResNet—to the particular job of classifying OA severity, we fine-tune them. Then, to aggregate predictions from several models for increased accuracy and resilience, ensemble learning techniques are applied. The outcomes of our experiments show how successful our strategy is, since our models diagnose OA severity levels with a high degree of accuracy. Our work advances the field of medical image analysis and has potential benefits for improving OA diagnosis and treatment.