RA VISION-X: A Deep Learning Framework for Detecting Rheumatoid Arthritis

Abstract Rheumatoid Arthritis (RA) is a long-term autoimmune disease that causes inflammation of the joints and may lead to severe pain, functional limitations, and permanent joint damage if not identified at an early stage. Accurate and timely diagnosis remains a major challenge due to the complexity of clinical symptoms and the dependence on expert interpretation of medical images. To address this issue, this paper introduces RA VISION-X, a deep learning-based framework for the automated detection of Rheumatoid Arthritis using medical imaging data. The proposed approach employs convolutional neural networks to learn meaningful features directly from joint images and perform reliable classification between RA-affected and healthy cases. The system is designed to minimize manual intervention while improving diagnostic consistency and efficiency. Experimental evaluation indicates that the proposed framework achieves promising accuracy and robustness, demonstrating its potential to support clinicians in early RA diagnosis and decision-making processes. Keywords - Rheumatoid Arthritis, Deep Learning, Convolutional Neural Networks, Medical Image Analysis, Automated Disease Detection, Computer-Aided Diagnosis