Automated Microaneurysms Detection in Fundus Images for Early Diagnosis of Diabetic Retinopathy

Abstract

The detection of microaneurysms at an early stage plays an imperative role to reduce the severity of diabetic retinopathy (DR) disease. As microaneurysm is a common sign of diabetic retinopathy, which appears as a red lesion in the fundus image. This paper deals with the classification of microaneurysms and non-microaneurysms. The proposed work follows four steps, specifically pre-processing, candidate extraction, extraction of texture features from candidate region using Gabor filter and finally classification based on extracted features. Support vector machine (SVM) has been used for classification of microaneurysms (MAs). The proposed approach has been tested on Indian diabetic retinopathy image dataset (IDRiD). The reported result of accuracy is 80.85%, specificity is 78.76% and sensitivity 76.75% highlights the effectiveness of the presented technique.