David, S. AlexKumar, V. DhilipPolat, KemalAlhudhaif, AdiNour, Majid2023-08-102023-08-102022David, S. A., Mahesh, C., Kumar, V. D., Polat, K., Alhudhaif, A., & Nour, M. (2022). Retinal blood vessels and optic disc segmentation using U-net. Mathematical Problems in Engineering, 2022, 1-11.1024-123X1563-5147http://dx.doi.org/10.1155/2022/8030954https://hdl.handle.net/20.500.12491/11469A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.eninfo:eu-repo/semantics/openAccessConvolution Neural-NetworkImageSensitivityDelineationExtractionAlgorithmLevelArticle10.1155/2022/803095420221112-s2.0-85125853516Q2WOS:000788019800006N/A