ADVANCED DIAGNOSTIC FRAMEWORK FOR RHEUMATOID ARTHRITIS THROUGH DEEP LEARNING ANALYSIS

Abstract

Rheumatoid Arthritis (RA) is characterised by joint abnormalities, swelling, and pain. Effective treatment and the prevention of irreparable joint injury depend on early and correct diagnosis.Strategies using convolutional neural networks and a dedicated deep-learning decision-support system are two examples of DL approaches (DLDSS). Automatic detection of key features indicative of Rheumatoid Arthritis (RA) is achieved by training on medical imaging data such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using well-established convolutional neural network-based models. The convolutional neural network (CNN) model learns to identify complicated patterns linked to RA progression through rigorous training on a big database of labelled photos. When it comes to detecting and classifying anomalies in joints impacted by RA, the fine-tuned model is lightning fast and very specific. The Decision Learning Support Systems for Deep Learning's (DLSS) primary objective is to improve the deep learning model's interpretability and provide treatment-related insights. The outcome of the convolutional neural network (CNN) model is integrated with clinical data, previous patient medical history, and other related biomarkers to generate a thorough decision-support framework (OC-1). In addition, this holistic method confirms accurate diagnosis, which permits a well-informed assessment of RA severity and therapy options for afflicted individuals. The proposed Decision Methodology underwent a comprehensive analysis utilising many datasets to assess its effectiveness in diagnosing rheumatoid arthritis, hence illustrating the applicability of our proposed system. In order to make better decisions, the CNN and DLDSS work together to leverage the context information. In order to accomplish this, it processes images using deep learning frameworks. Finally, RA Predictive Diagnosis shows significant improvement in medical intensive-care unit analysis. With the help of cutting-edge CNN algorithms and a brand new DLDSS, the system can enhance RA diagnosis accuracy and efficacy, which in turn improves patient outcomes and allows for more personalised treatment plans.