Deep Learning-based prediction of Diabetic Retinopathy using CLAHE and ESRGAN for Enhancement

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly.
The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep
learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than
previous methods. The suggested method presents two scenarios: case 1 with image enhancement
using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction
with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without
image enhancement. Augmentation techniques were then performed to generate a balanced dataset
utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-
Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for
case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of
DR. It was demonstrated that using CLAHE and ESRGAN improves a model’s performance and
learning ability.