Automatic detection and severity classification of diabetic retinopathy

Diabetic retinopathy (DR) is a leading cause of preventable blindness caused by damaged blood vessels in the eye, if not treated early on. The aim of this research work was to develop a method for the automatic detection of Diabetic Retinopathy and proposing a model for deciding the progression/severity using fundus images. The method was developed so that DR can be detected in an effective and efficient manner before causing damage to the eye, without the presence of an ophthalmologist. The manual screening requires the presence of an ophthalmologist and the resource of time. Detecting exudates is important for the diagnosis of DR. The approach adopted was two-fold: i. extracting features of interest from the images i.e. the blood vessels, optic disc (OD), exudates and microaneurysms by using morphological operations and ii. classifying its progression/severity as either mild or moderate by using the support vector machine (SVM) classifier for helping Ophthalmologists. The performance of the proposed method has been assessed by an ophthalmologist and approved. This paper contributes towards the field of automatic detection of anomalous structures and their severity.