Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture

Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning for 20 epochs, reached 99.8% training accuracy and 91% testing accuracy. Additionally, a web app was developed using Streamlit, enabling users to upload knee X-ray images and receive real-time predictions of OA severity. The results demonstrate the effectiveness of CNN and transfer learning with VGG16 in knee OA classification, offering a promising tool for early diagnosis in clinical settings.