Comparative Study of Diabetic Retinopathy Detection Using Machine Learning Techniques

Abstract

Abstract: Untreated diabetic retinopathy, a condition brought on by unmanaged chronic diabetes, can result in total blindness. In order to avoid the serious side effects of diabetic retinopathy, early medical diagnosis of diabetic retinopathy and its medical treatment are imperative. Ophthalmologists must spend a lot of time manually diagnosing diabetic retinopathy, and patients must endure a lot of discomfort throughout this process. With the use of an automated technology, we can rapidly identify diabetic retinopathy and conveniently continue treatment to prevent further damage to the eye. Exudates, haemorrhages, and micro aneurysms are three features that this study suggests extracting using machine learning. These features are then classified using a hybrid classifier, which combines support vector machines, k nearest neighbours, random forests.