Enhancing Rheumatoid Arthritis Diagnosis Through Fuzzy Logic And Support Vector Machines In Hand Radiography

Rheumatic Autoimmune Disease is a chronic autoimmune disorder primarily affecting the joints, often leading to irreversible damage if not diagnosed and treated promptly. Hand radiography is a crucial diagnostic modality for assessing RA, as it enables the visualization of structural changes in the joints. This research explores the integration of Fuzzy Logic and Support Vector Machines (SVM) to enhance the accuracy of RA diagnosis and contribute to more effective treatment strategies based on hand radiographic images. The proposed approach involves the development of a comprehensive system that leverages Fuzzy Logic to handle the inherent uncertainties and imprecisions associated with RA diagnosis. Fuzzy Logic allows for a nuanced representation of the uncertainties in medical images, capturing subtle variations that may be indicative of early-stage RA. Additionally, Support Vector Machines are employed for their capability to classify and distinguish complex patterns within the radiographic data. The methodology begins with preprocessing steps to enhance the quality of hand radiographic images, followed by feature extraction to highlight relevant patterns associated with RA. Fuzzy Logic is then applied to model the uncertainty in these features, accommodating the inherent variability in RA manifestations. The Fuzzy Inference System (FIS) is designed to assign degrees of membership to different diagnostic categories, aiding in the creation of a more nuanced and flexible diagnostic framework.