Comprehensive Analysis for Rheumatoid Arthritis Detection using Deep Learning Models

Rheumatic arthritis (RA) is a Continual autoimmune condition that mostly impacts the joints, causing discomfort, Inflammation, leading to eventual joint damage deterioration if not properly recognized and treated. Medical imaging relies heavily on X-ray images for the early identification and ongoing assessment of RA development. Although deep learning techniques have shown promising outcomes in automating the diagnosis of rheumatoid arthritis (RA) from X-ray images, there are still various hurdles that need to be overcome to achieve a high level of accuracy and reliability. This paper introduces a thorough framework for the automated identification of Rheumatic arthritis (RA) using radiographs. The emphasis is on advanced deep learning models and optimization strategies to improve accuracy. It involves analyzing several convolutional neural networks (CNNs) and their structures, such as AlexNet, VGG, ResNet, and DenseNet, and carefully assessing their performance metrics and practical usefulness. Furthermore, the study explores optimization methods including data enhancement, preprocessing methods, pre-trained model utilization, regularization, also advanced training strategies to improve the accuracy and resilience of RA detection models. Comparative studies are carried out to determine the most efficient optimization solutions for this particular assignment. To discusses the constraints related to data, interpretability of models, integration into clinical practice, generalization, and resilience, and suggests alternative remedies such as improved neural network structures, federated learning methods, and multimodal data integration techniques.