A hybrid BPSO-SVM for feature selection and classification of ocular health

Abstract

Glaucoma and diabetic retinopathy are the most common eye diseases and the leading cause of blindness around the world. The prime objective of this study is to devise and develop an experimental computer-aided diagnosis system to provide an efficient way for assisting the ophthalmologist in early detection of ocular diseases such as glaucoma and diabetic retinopathy. The proposed technique follows three stages: Pre-processing, feature selection and classification. Initially, the fundus image is pre-processed to extract the green channel image, and the obtained green channel image is further enhanced using contrast limited adaptive histogram equalisation technique. Three different kinds of features: Clinical features, transform domain features and structural features are utilised to extract the relevant information from the enhanced fundus images. To avoid redundant information, an improved feature selection mechanism is used to select the optimum set of features from the extracted features. Subsequently, the selected features are used to train the support vector machine classifier for the classification of the retinal diseases with 10-fold cross-validation. The performance of the proposed method is assessed using eight different quantitative evaluation measures. The experimental results demonstrate the effectiveness of the proposed work over prior works for the early detection of ocular diseases. © 2020 The Authors.