AN OPTIMIZED DEEP LEARNING MODEL FOR EARLY DETECTION OF RHEUMATOID ARTHRITIS USING KNEE X-RAYS

Rheumatoid arthritis (RA) is a chronic autoimmune condition that requires early diagnosis to prevent irreversible joint damage. While deep learning models have shown promise in automated RA detection, existing approaches suffer from data imbalance, suboptimal feature extraction, and poor generalizability, limiting their clinical applicability. This study proposes an optimized Convolutional Neural Network (CNN) model, incorporating image augmentation and class balancing techniques to improve RA detection accuracy from knee X-ray images. Unlike previous studies, which often overlook the impact of data augmentation on model performance, our work demonstrates its effectiveness in addressing data imbalance and enhancing model robustness. We trained and validated our model using the Kaggle knee X-ray dataset, applying image augmentation to expand training samples and oversampling to balance class distributions. The CNN was optimized through rigorous hyperparameter tuning. Our optimized CNN model achieved a high accuracy of 94%, outperforming baseline deep learning models. Data augmentation and oversampling significantly improved model performance, proving their effectiveness in medical imaging tasks. Our findings establish a novel deep learning framework for RA detection, demonstrating the importance of data augmentation and optimization in improving diagnostic accuracy. This work contributes to the growing field of AI in medical imaging by offering a scalable and interpretable solution for automated RA detection