A Hybrid Classifier for the Detection of Microaneurysms in Diabetic Retinal Images

Diabetic Retinopathy (DR) is a chronic, progressive ocular disease in which the human retina is affected due to an increasing amount of insulin in blood. The prevalence and incidence of DR is associated with people having prolonged hyperglycaemia and other symptoms linked with diabetes mellitus. DR, if not detected and treated in time poses threat to the patient's vision ultimately causing total blindness. Among the various clinical signs, microaneurysms (MAs) appear as the early and first sign of DR. The accurate and reliable detection of microaneurysms is a challenging problem owing to its tiny size and low contrast. Successful detection of microaneurysms would be more useful for a proper planning and appropriate treatment of the disease at the early stage. The work mainly envisages the improvement of the classification accuracy by employing a hybrid classifier which combines Support Vector Machine (SVM), Naive Bayes Classifier and the decision tree. In contrast to many other classifiers the proposed classifier works efficiently, proves to be simple in terms of computational complexity and also gives good results. The performance is evaluated using publicly available retinal image database DIARETDB1. The hard decision fusion among the three classifiers carried out using the majority voting rule gives accuracy, sensitivity and specificity of 82.2916%, 82.692%, 81.818% respectively.