Automated Retinal Image Analysis to Detect Optic Nerve Hypoplasia

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dc.authorscopusid57190743818en_US
dc.authorscopusid15077642900en_US
dc.authorscopusid56063484400en_US
dc.contributor.authorÇelik, Canan
dc.contributor.authorYucedag, Ibrahim
dc.contributor.authorAkcam, Hanife Tuba
dc.date.accessioned2024-08-23T16:03:22Z
dc.date.available2024-08-23T16:03:22Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractIdentification of the optic disc and fovea is crucial for automating the diagnosis and screening of retinal diseases. Based on quantitative calculations, this study presents a decision support system for doctors that automatically detect optic nerve hypoplasia. For disease diagnosis, U -Net architecture is used, which uses a pre -trained ResNet encoder to segment the optic disc and fovea structures. An important aspect of the proposed technique is that pretrained ResNet and U -Net are used together, providing robust performance in the detection of optic nerve hypoplasia. Our proposed architecture was tested on retinal images from Messidor, Diaretdb1, DRIVE, HRF, APTOS, and IDRID. In addition, a special database called ONH-NET was created based on 189 retinal images obtained from D & uuml;zce University, Department of Ophthalmology. Messidor database test images showed, 0. 9069 IOU Score, 0.9626 Sensitivity, 0.9411 Precision, 0.9974 Accuracy and 0.9505 dice -coefficient values in optic disc detection, and 0.8282 IOU score, 0.8442 sensitivity, 0.8252 precision, 0.8992 Accuracy, 0.7873 dice coefficient values were obtained in fovea detection. We computed diameter optic disc to macula radius ratios from segmented optic disc and fovea for screening optic nerve hypoplasia and achieved 100% success.en_US
dc.identifier.doi10.5755/j01.itc.53.2.35152
dc.identifier.issn1392-124X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85197550608en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.5755/j01.itc.53.2.35152
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13705
dc.identifier.volume53en_US
dc.identifier.wosWOS:001266770800014en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKaunas Univ Technologyen_US
dc.relation.ispartofInformation Technology And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectImage Segmentationen_US
dc.subjectOptic Discen_US
dc.subjectFoveaen_US
dc.subjectMaculaen_US
dc.subjectU-Neten_US
dc.subjectConvolutional Neural-Networksen_US
dc.subjectDisc Segmentationen_US
dc.subjectBlood-Vesselsen_US
dc.subjectMorphologyen_US
dc.subjectNeten_US
dc.subjectCupen_US
dc.titleAutomated Retinal Image Analysis to Detect Optic Nerve Hypoplasiaen_US
dc.typeArticleen_US

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