Automated identification of diabetic retinopathy stages using support vector machine

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). Early detection of diabetic retinopathy is crucial to prevent blindness. In this work, we have proposed a computer based approach for the detection of diabetic retinopathy stage using color fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of blood vessels, hard exudates and the contrast, homogeneity. The features are fed to the support vector machine (SVM). We demonstrate a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.