Abstract
Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it would eventually lead to macular edema. In this paper, we designed three deep learning models that would detect the severity of diabetic retinopathy from images of the retina and also determine if it would lead to macular edema. Since our dataset was a small one, we employed three techniques for generating images from the ones we have, the techniques are Brightness, color and, contrast (BCC) enhancing, Color jitters (CJ), and Contrast Limited Adaptive Histogram Equalization (CLAHE). After the dataset was ready, we used it to train the ResNet50, VGG16, and VGG19 models both for determining the severity of the retinopathy and also the chances of macular edema. After validation, the models yielded very reasonable results.