Detection of Diabetic Retinopathy Using Convolutional Neural Networks

Abstract

In the modern world, diabetes is one of the major health problems that affects humans all around the world. Lack of early detection, prolonged diabetics, might lead to medical complications such as heart problems, eye vision problems, skin issues, etc. Diabetic retinopathy (DR) is a frequent abnormality of diabetics. To help patients with the early detection of diabetic retinopathy, in this paper, we propose a computer vision-based technique to analyze and predict diabetes from the retinal input images. Image preprocessing, segmentation, and feature extraction steps are applied. Convolutional neural networks (CNN) and Support Vector Machine (SVM) are trained with diabetic and non-diabetic retinal images. Results show CNN reports better accuracy in DR compared to SVM.