Retinal disease classification using optical coherence tomography angiography images

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dc.authorid0009-0000-3453-1502
dc.authorid0009-0007-4212-5484
dc.authorid0000-0002-8649-6013
dc.authorid0000-0001-6309-4524
dc.contributor.authorAydın, Ömer Faruken_US
dc.contributor.authorNazlı, Muhammet Serdaren_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorTurkan, Yaseminen_US
dc.date.accessioned2025-08-15T11:38:41Z
dc.date.available2025-08-15T11:38:41Z
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 the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 122E509.en_US
dc.description.abstractOptical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality widely used for the detailed visualization of retinal microvasculature, which is crucial for diagnosing and monitoring various retinal diseases. However, manual interpretation of OCTA images is labor-intensive and prone to variability, highlighting the need for automated classification methods. This study presents an aproach that utilizes transfer learning to classify OCTA images into different retinal disease categories, including age-related macular degeneration (AMD) and diapethic retinopathy (DR). We used the OCTA-500 dataset [1], the largest publicly available retinal dataset that contains images from 500 subjects with diverse retinal conditions. To address the class imbalance, we employed k-fold cross-validation and grouped various other conditions under the 'OTHERS' class. Additionally, we compared the performance of the ResNet50 model with OCTA inputs to that of the ResNet50 and RetFound (Vision Transformer) models with OCT inputs to assess the efficiency of OCTA in retinal condition classification. In the three-class (AMD, D R, Normal) classification, ResNet50-OCTA o utperformed ResNet50-OCT, but slightly underperformed compared to RetFound-OCT, which was pretrained on a large OCT dataset. In the four-class (AMD, DR, Normal, Others) classification, ResNet50-OCTA and RetFound-OCT achieved similar classification a ccuracies. This study establishes a baseline for retinal condition classification using the OCTA-500 dataset and provides a comparison between OCT and OCTA input modalities.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationAydın, Ö. F., Nazlı, M. S, Tek, F. B. & Turkan, Y. (2024). Retinal disease classification using optical coherence tomography angiography images. UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering, 884-889. doi:10.1109/UBMK63289.2024.10773610en_US
dc.identifier.doi10.1109/UBMK63289.2024.10773610
dc.identifier.endpage889
dc.identifier.isbn9798350365887
dc.identifier.scopus2-s2.0-85215510113
dc.identifier.scopusqualityN/A
dc.identifier.startpage884
dc.identifier.urihttps://hdl.handle.net/11729/6616
dc.identifier.urihttps://doi.org/10.1109/UBMK63289.2024.10773610
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.subjectClass imbalanceen_US
dc.subjectDeep learningen_US
dc.subjectImage classificationen_US
dc.subjectk-Fold Cross- Validationen_US
dc.subjectOCTAen_US
dc.subjectOptical coherence tomography angiographyen_US
dc.subjectResNet50en_US
dc.subjectRetinal diseasesen_US
dc.subjectEye protectionen_US
dc.subjectOptical coherence tomographyen_US
dc.subjectOptical tomographyen_US
dc.subjectCoherence tomographyen_US
dc.subjectK fold cross validations
dc.subjectOptical-en_US
dc.subjectAngiographyen_US
dc.titleRetinal disease classification using optical coherence tomography angiography imagesen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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