An Intelligent System for Diagnosis of Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) or diabetic eye disease is a medical condition in which damage occurs to the retina due to diabetes mellitus causing blindness. Early detection of DR helps in preventing the onset of blindness in diabetic patients. Numerous intelligent models have been proposed for early detection of DR. However, models incorporating support vector machine (SVM) for early detection of DR have been very rare. Therefore, this paper proposes a model called intelligent system for diabetic retinopathy (ISDR) for early detection of DR using SVM. The fundus images captured with the help of a digital fundus camera (DFC) are used as inputs to the proposed model. The fundus images are initially enhanced and then segmented to extract two most prominent features for the early detection of DR, namely foveal avascular zone (FAZ) and microaneurysms (MA). The extracted features are used as inputs to SVM for classification of the fundus images to classify them as 0—no DR, 1—mild DR, 2—moderate DR, 3—non-proliferative DR (NPDR) and 4—proliferative DR (PDR). The model is validated by the Kaggle dataset. It is observed from the experimental results that early detection of DR is feasible using ISDR.