Rheumatoid arthritis detection using image processing

Abstract

Rheumatoid Arthritis (RA) may be a general disease characterized by inflammation, discomfort, and tenderness of the joints and might involve additional body part organs in severe cases. Leading to increased vascular disorder in the zone of inflammatory tissue, joint autoimmune lesions are associated with elevated fever. The detection of RA usually involves blood sample tests. This thesis proposes a novel methodology of detection by processing the Xray images. This automated system requires clear Xray images, which after preprocessing and segmentation using Support Vector Machine implemented via MATLAB gives a clear classification about the abnormal and normal images. Different output parameters were used to assess separation tasks. The accuracy of the model section has improved to the use of an optimized SVM network. The proposed model was effective in accurately separating the samples.