Diabetic　retinopathy　(DR)　is　a　primary　cause　of　blindness　in　which　damage　occurs　to　the　retina　due　to　an　accretion　of　sugar　levels　in　the　blood.　Therefore,　prior　detection,　classification,　and　diagnosis　of　DR　can　prevent　vision　loss　in　diabetic　patients.　We　proposed　a　novel　and　hybrid　approach　for　prior　DR　detection　and　classification.　We　combined　distinctive　models　to　make　the　DR　detection　process　robust　or　less　error-prone　while　determining　the　classification　based　on　the　majority　voting　method.　The　proposed　work　follows　preprocessing　feature　extraction　and　classification　steps.　The　preprocessing　step　enhances　abnormality　presence　as　well　as　segmentation;　the　extraction　step　acquires　merely　relevant　features;　and　the　classification　step　uses　classifiers　such　as　support　vector　machine　(SVM),　K-nearest　neighbor　(KNN),　and　binary　trees　(BT).　To　accomplish　this　work,　multiple　severities　of　disease　grading　databases　were　used　and　achieved　an　accuracy　of　98.06%,　sensitivity　of　83.67%,　and　100%　specificity.　©　2013　IEEE.