Detecting Diabetic Retinopathy in Fundus Images using Combined Enhanced Green and Value Planes (CEGVP) with k-NN

Abstract

Diabetic Retinopathy (DR) is a disease that causes damage to the blood vessels of the retina, especially in patients having high uncontrolled blood sugar levels, which may lead to complications in the eyes or loss of vision. Thus, early detection of DR is essential to avoid complete blindness. The automatic screenings through computational techniques would eventually help in diagnosing the disease more accurately. The traditional DR detection techniques identify the abnormalities such as microaneurysms, hemorrhages, hard exudates, and soft exudates from the diabetic retinopathy images individually. When these abnormalities occur in combination, it becomes difficult to predict them and the individual detection (traditional 4 class classification) accuracy decreases. Hence, there is a need to have separate combinational classes (16 class classification) that help to classify these abnormalities in a group or one by one. The objective of our work is to develop an automated DR prediction scheme that classifies the abnormalities either individually or in combination in retinal fundus images. The proposed system uses Combined Enhanced Green and Value Planes (CEGVP) for processing the fundus images, Principal Component Analysis (PCA) for feature extraction, and k-nearest neighbor (k-NN) for classification of DR. The suggested technique yields an average accuracy of 97.11 percent using a k-NN classifier. This is the first time that a 16-class classification is initiated that precisely gives the ability and flexibility to map the combinational complexity in a single step. The proposed method can assist ophthalmologists in efficiently detecting the abnormalities and starting the diagnosis on time.