Affiliation:
1. Computer Science and Engineering Department, Qatar University, Doha P.O. Box 2713, Qatar
Abstract
Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.
Funder
Qatar University Research Fund in Qatar
Reference44 articles.
1. MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms;Luo;Pattern Recognit.,2021
2. Sebastian, A., Elharrouss, O., Al-Maadeed, S., and Almaadeed, N. (2023). A Survey on Deep-Learning-Based Diabetic Retinopathy Classification. Diagnostics, 13.
3. Retinal vessel segmentation using neural network;Thangaraj;IET Image Process.,2018
4. Sebastian, A., Elharrouss, O., Al-Maadeed, S., and Almaadeed, N. (2023). A Survey on Diabetic Retinopathy Lesion Detection and Segmentation. Appl. Sci., 13.
5. (2023, March 30). Drive Dataset. Available online: https://drive.grand-challenge.org/.
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