Version 1
: Received: 9 February 2023 / Approved: 13 February 2023 / Online: 13 February 2023 (14:33:05 CET)
How to cite:
Alwakid, G.; Gouda, W.; Humayun, M. Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN. Preprints2023, 2023020218. https://doi.org/10.20944/preprints202302.0218.v1
Alwakid, G.; Gouda, W.; Humayun, M. Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN. Preprints 2023, 2023020218. https://doi.org/10.20944/preprints202302.0218.v1
Alwakid, G.; Gouda, W.; Humayun, M. Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN. Preprints2023, 2023020218. https://doi.org/10.20944/preprints202302.0218.v1
APA Style
Alwakid, G., Gouda, W., & Humayun, M. (2023). Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN. Preprints. https://doi.org/10.20944/preprints202302.0218.v1
Chicago/Turabian Style
Alwakid, G., Walaa Gouda and Mamoona Humayun. 2023 "Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN" Preprints. https://doi.org/10.20944/preprints202302.0218.v1
Abstract
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.
Keywords
diabetic retinopathy; Vision loss; Deep learning; CLAHE; ESRGAN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
