A Novel Approach for the Early Detection of Rheumatoid Arthritis on Hand and Wrist Using Convolutional Reinforcement Learning Techniques

Abstract

A reliable provocative issue which impacts the joints by harming the body's tissue is called as Rheumatoid arthritis . As needs be, the ID and ID of rheumatoid arthritis by hand, particularly during its unanticipated turn of events or pre-expressive stages, requires an extraordinary construction analysis. The standard end technique for Rheumatoid Arthritis (RA) recalls for the assessment of hands and feet radiographs. Notwithstanding, still for clinical experts it winds up being an unconventional endeavor considering the way that regularly the right completion of the disease relies on the exposure of unfathomably subtle changes in the typical eye. In this work, we built up a design using Convolutional Neural Networks (CNN) and Reinforcement Learning Technique for detecting  RA from hand and wrist MRI. For this we took 564 cases(real information) which provided a precision of 100 %. Compared to the existing system, the system showed a high performance with very good results. This model is highly recommended to detect Rheumatoid arthritis automatically ,without human intervention.