Detection of Glaucoma in Retinal Images Based on Multiobjective Approach

Abstract

Glaucoma is one of the major causes of blindness. Glaucoma is a condition due to increased pressure within the eyeball, causing progressive, irreversible and gradual loss of sight. It can be prevented only detection in disc ratio (CDR), ratio of blood vessel area in interior and superior side of the blood vessel in the nasal temporal side extracted from retinal fundus images. Reducing the number of features and reducing the error rate are two conflicting objectives. The proposed methodology aims to explore the use of Differential Evolution based multi objective feature selection technique to select a subset of features which minimize both our conflicting objectives. The performance of selected subset of features has been evaluated using KNN classification technique. It has been observed that said extracted subset of five features, KNN classification achieved better accuracy than any other subset of features.