Alwakid, G.; Gouda, W.; Humayun, M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare2023, 11, 863.
Alwakid, G.; Gouda, W.; Humayun, M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare 2023, 11, 863.
Alwakid, G.; Gouda, W.; Humayun, M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare2023, 11, 863.
Alwakid, G.; Gouda, W.; Humayun, M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare 2023, 11, 863.
Abstract
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.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
