Retinal blood vessels and optic disc segmentation using U-Net

dc.authorid0000-0002-8740-6710en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.contributor.authorDavid, S. Alex
dc.contributor.authorKumar, V. Dhilip
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorNour, Majid
dc.date.accessioned2023-08-10T06:14:39Z
dc.date.available2023-08-10T06:14:39Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA 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.en_US
dc.identifier.citationDavid, 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.en_US
dc.identifier.doi10.1155/2022/8030954
dc.identifier.endpage11en_US
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85125853516en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/8030954
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11469
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000788019800006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofMathematical Problems in Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolution Neural-Networken_US
dc.subjectImageen_US
dc.subjectSensitivityen_US
dc.subjectDelineationen_US
dc.subjectExtractionen_US
dc.subjectAlgorithmen_US
dc.subjectLevelen_US
dc.titleRetinal blood vessels and optic disc segmentation using U-Neten_US
dc.typeArticleen_US

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