ARTICLE | doi:10.20944/preprints202302.0218.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: diabetic retinopathy; Vision loss; Deep learning; CLAHE; ESRGAN
Online: 13 February 2023 (14:33:05 CET)
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.