CLASSIFICATION OF GLAUCOMA USING SIMPLIFIED-MULTICLASS SUPPORT VECTOR MACHINE

Abstract

Detection and diagnosis of glaucoma disease of eye fundus images at early stage is very important as this disorder leads to complete loss of vision if ignored. Usually, 80–90% of glaucoma cases are analyzed manually by ophthalmologists. As the manual analysis varies from one expert to other, diagnosis cannot be effective. Hence, there is a need for automatic assessment of glaucoma disease using computer aided diagnosis (CAD). Many researchers have devised several CAD techniques for glaucoma analysis using various classification techniques. However, most of the classifiers are efficient only for two level classification to detect whether disease is glaucoma or not. But, glaucoma disease has several stages and demands multilevel approaches with high degree of classification accuracy. Among several multiclass methods, literature suggests multiclass support vector technique (MSVM) as a better performing statistical classifier. However, many MSVMS suffer from data loss during training phase. To address this issue, a robust hybrid classification approach consisting of Naïve Bayes binary classifier in the first stage and simplified multiclass support vector machine (Sim-MSVM) in the second stage is proposed in this paper.