Diabetic Retinopathy Using Deep Learning

Abstract

Untreated diabetic retinopathy, a complication of uncontrolled diabetes, may lead to total blindness if not addressed promptly. Consequently, in order to avoid the serious complications of diabetic retinopathy, it is crucial to diagnose the condition early and treat it medically. Patients go through a lot of pain and suffering as ophthalmologists manually identify diabetic retinopathy. With the use of an automated method, diabetic retinopathy may be detected more rapidly, allowing for easier follow-up therapy to prevent more eye damage. This paper presents a machine learning strategy for feature extraction including exudates, hemorrhages, and micro aneurysms. The strategy involves a hybrid classifier that integrates support vector machine, k closest neighbour, random forest, logistic regression, and multilayer perceptron networks. To further assist in DR stage image recognition, for instance to detect blood vessels, future research may center on applying object identification techniques based on convolutional neural networks (CNNs).