A Novel SDMFO-MBSVM-Based Segmentation and Classification Framework for Glaucoma Detection Using OCT and Fundus Images

Abstract

Glaucoma is an eye disease that causes loss of vision and blindness by damaging a nerve in the back of the eye called optic nerve. The optic nerve collects the visual information from the eyes and transmits to the brain. Glaucoma is mainly caused by an abnormal high pressure in the eyes. Over time, the increased pressure can erode the tissues of optic nerve, leading to vision loss or blindness. If it is diagnosed in advance, then only it can prevent the vision loss. To diagnose the glaucoma, it must accurately differentiate between the optic disc (OD), optic cup (OC), and the retinal nerve fiber layer (RNFL). The segmentation of the OD, OC, and RNFL remains a challenging issue under a minimum contrast image of boundaries. Therefore, in this study, an innovative method of Hybrid Symbiotic Differential Evolution Moth-Flame Optimization (SDMFO)-Multi-Boost Ensemble and Support Vector Machine (MBSVM)-based segmentation and classification framework is proposed for accurately detecting the glaucoma disease. By using Group Search Optimizer (GSO), the affected parts of the OD, OC and RNFL are segmented. The proposed SDMFO-MBSVM method is executed in MATLAB site, its performance is analyzed with three existing methods. From the comparison, the accuracy of the proposed method in OD segmentation gives better results of 3.37%, 4.54% and 2.22%, OC segmentation gives better results of 2.22%, 3.37% and 4.54%, and RNFL segmentation gives the better results of 3.37%, 97.21% and 5.74%.