ARCHITECTURE AND PERFORMANCE OF GLAUCOMA DETECTION SYSTEM ON THE BASIS COMBINING CLASSIFIERS - M. , Sreedhar; Bhaskar, Radhika (International Journal of Psychosocial Rehabilitation, 2020)
Glaucoma is a category of eye disorders that are critical to good vision, destroying the optic nerve. The effect is often an abnormally high eye pressure. Glaucoma is one of the main blindness causes for people over 60 years old. It is more common in older adults, though, at all ages. The early diagnosis of glaucoma is required. In this study, the Glaucoma Detection System (GDS) using combining classifiers is presented. The GAD system uses Naïve Bayes (NB) and Random Forest (RF) classifiers for the glaucoma detection. The optic cup and optic disc are mainly used to detect the abnormalities in the fundus images. Initially, the given input fundus images—optic cup and optic disc—are extracted and Region of Interest (ROI) is detected. Then the energy features are extracted and stored in the database. Then the extracted features are used as the input to predict the NB and RF classifiers, which are used as combining classifiers for glaucoma detection.