Sometimes when diabetic retinopathy (DR) is found and treated quickly, vision loss can indeed be spared. This study deploys a deep learning (DL) model that can discover all 5 stages of DR more accurately than other methods. The proposed methodology shows two cases scenarios: case 1 with image enhancement using CLAHE and ESRGAN, and case 2 without image enhancement. Augmentation techniques are then employed to produce a balanced dataset with the identical criteria for both scenarios. The generated model using DenseNet-121 on the APTOS dataset outperformed other approaches for locating the 5 stages of DR, with an accuracy of 98.7 percent for case 1 and 81.2 percent for case 2. Using CLAHE and ESRGAN was shown to improve a model's performance and ability to learn.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. Following are the main 5 DR stages: none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all 5 stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using 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 are 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.