Osteoarthritis detection system using transfer learning

Abstract

Osteoarthritis is a degenerative joint disease that leads to cartilage deterioration, pain, stiffness, and reduced mobility, particularly in the knee joint. Early detection of osteoarthritis is important for preventing severe joint damage and improving treatment outcomes. Traditional diagnosis mainly depends on manual interpretation of knee x-ray images by radiologists, which can be time-consuming and may lead to variations in diagnosis. To address this challenge, this study proposes an automated osteoarthritis detection system using deep learning techniques. The proposed system utilizes the efficientnetb0 convolutional neural network model to analyze knee x-ray images and classify the severity of osteoarthritis. Image preprocessing techniques are applied to enhance image quality before training the model. The trained model categorizes knee x-ray images into three classes: healthy, moderate, and severe. A web-based application developed using python, django, and mysql allows users to upload knee x-ray images and obtain prediction results with confidence scores. In addition to automated detection, the system provides exercise recommendations, diet plans, and doctor appointment booking features to assist patients in managing osteoarthritis effectively. Experimental results show that the efficientnetb0 model achieves an accuracy of approximately 0.97, demonstrating its effectiveness for medical image classification and osteoarthritis detection.