DEVELOPMENT OF AN EXPERT SYSTEM BASED ON FUZZY LOGIC FOR EARLY DIAGNOSIS OF GLAUCOMA

Abstract

Nowadays, decision-making systems that rely on images are becoming increasingly crucial, especially in the medical field. Images have become a fundamental tool for clinical research and diagnosing illnesses. In the case of glaucoma, a disease that can damage the optic nerve head and result in irreversible vision loss, a new Fuzzy Expert System has been developed for early diagnosis. Original ONH images are preprocessed with filters to remove noise, followed by using the Canny detector algorithm to detect contours. Key parameters are then extracted by identifying elliptical forms of the optic disc and excavation using the Randomized Hough Transform. A classification algorithm based on fuzzy logic is used to assess patients' conditions, taking into account both instrumental parameters and risk factors such as age, race, and family history. The system is tested on a dataset of ophthalmologic images, showing a significant improvement in predictions compared to existing methods, with over 96% accuracy in identifying cases suspected to have glaucoma.