Early Detection of Diabetic Retinopathy through Machine Learning Techniques

Diabetic Retinopathy (DR) is progressive syndrome that leads to loss of vision if not detected and treated. Retina is inner tunic of the eyeball which is capillary and delicate transparent membrane. It is high developed tissue of eye which plays a major role for vision. Retina is the source for detection of many disorders. Part of retina with optic disc can be viewed through optamoloscope and termed as fundus image which is a basis of diagnosis for DR. DR can be categorized as Proliferative Diabetic Retinopathy (PDR), Diabetic Maculopathy, Nonproliferative Diabetic Retinopathy (NPDR) and Advanced Diabetic Eye Disease. Machine Learning (ML) techniques play a vital role in early detection of DR. In this paper a review on the existing techniques with open issues to be addressed is presented for diagnosing DR and model is proposed to consider the features namely Microaneurysms, Retinal Hemorrhages, Hard exudates, Cotton wool Spots, Neovascularization for classification of DR. These features can be combined with hypertension to predict other disorders like stroke, chronic heart disease, renal dysfunction, cardiovascular mortality and so on which overcome the need of other preliminary checkup .The complete profile of disorders for a diabetic patient can be deduced by the retinal fundus image.