Segmentation based classification of retinal diseases in OCT images

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dc.authorid0009-0000-6524-5488
dc.authorid0000-0002-8649-6013
dc.authorid0000-0001-6309-4524
dc.contributor.authorEren, Öyküen_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorTurkan, Yaseminen_US
dc.date.accessioned2025-08-15T11:02:00Z
dc.date.available2025-08-15T11:02:00Z
dc.date.issued2024
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.descriptionThis study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 122E509. The authors thank to TUBITAK for their support.en_US
dc.description.abstractVolumetric optical coherence tomography (OCT) scans offer detailed visualization of the retinal layers, where any deformation can indicate potential abnormalities. This study introduced a method for classifying ocular diseases in OCT images through transfer learning. Applying transfer learning from natural images to Optical Coherence Tomography (OCT) scans present challenges, particularly when target domain examples are limited. Our approach aimed to enhance OCT-based retinal disease classification by leveraging transfer learning more effectively. We hypothesize that providing an explicit layer structure can improve classification accuracy. Using the OCTA-500 dataset, we explored various configurations by segmenting the retinal layers and integrating these segmentations with OCT scans. By combining horizontal and vertical cross-sectional middle slices and their blendings with segmentation outputs, we achieved a classification a ccuracy of 91.47% and an Area Under the Curve (AUC) of 0.96, significantly outperforming the classification of OCT slice images.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEren, Ö., Tek, F. B. & Turkan, Y. (2024). Segmentation based classification of retinal diseases in OCT images. UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering, 890-895. doi:10.1109/UBMK63289.2024.10773527en_US
dc.identifier.doi10.1109/UBMK63289.2024.10773527
dc.identifier.endpage895
dc.identifier.isbn9798350365887
dc.identifier.scopus2-s2.0-85215513048
dc.identifier.scopusqualityN/A
dc.identifier.startpage890
dc.identifier.urihttps://hdl.handle.net/11729/6615
dc.identifier.urihttps://doi.org/10.1109/UBMK63289.2024.10773527
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTurkan, Yaseminen_US
dc.institutionauthorid0000-0001-6309-4524
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofUBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAge-related macular degenerationen_US
dc.subjectDeep learningen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectOCTen_US
dc.subjectRetinal disease classificationen_US
dc.subjectSegmentationen_US
dc.subjectAdversarial machine learningen_US
dc.subjectContrastive Learningen_US
dc.subjectEye protectionen_US
dc.subjectImage segmentationen_US
dc.subjectOptical tomographyen_US
dc.subjectTransfer learningen_US
dc.subjectCoherence tomographyen_US
dc.subjectDisease classificationen_US
dc.subjectOptical-en_US
dc.subjectRetinal diseaseen_US
dc.subjectOptical coherence tomographyen_US
dc.titleSegmentation based classification of retinal diseases in OCT imagesen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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