Enhancing Diabetic Retinopathy Screening Through Deep Learning

One common eye ailment that can cause blindness is diabetic retinopathy (DR). In order to help with diagnosis, DR picture grading goes beyond conventional binary classification techniques by automatically identifying the degree of DR. Accurate DR grading in medical imaging depends on identifying minute details like lesions and microaneurysms, yet these are easily obscured by other features in the eye pictures. Both in related domains and in the diagnosis of drug resistance itself, convolutional neural networks (CNNs) have demonstrated efficacy. Nevertheless, the high expense of acquiring sizable, labeled datasets and the discrepancies in diagnosis amongst physicians limit their efficacy. In this work, we present an automated deep learning method that uses a single human retinal picture to determine the stage of diabetic retinopathy