This project implements computer vision and deep learning to analyze medical imaging for rheumatoid arthritis detection. Leveraging convolutional neural networks (CNNs), it processes diagnostic images to identify and classify multiple severity levels of arthritis. By training on labeled datasets, the model learns visual patterns associated with disease progression, enabling automated, high-accuracy predictions that support clinical decision-making. This approach aims to enhance early detection, reduce manual interpretation errors, and provide scalable tools for assisting healthcare professionals in reliable arthritis assessment.