Retinal disease classification from bimodal OCT and OCTA using a CNN-ViT hybrid architecture

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dc.authorid0009-0000-3453-1502
dc.authorid0000-0002-8649-6013
dc.authorid0000-0001-6309-4524
dc.contributor.authorAydın, Ömer Faruken_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorTurkan, Yaseminen_US
dc.date.accessioned2026-03-06T11:02:23Z
dc.date.available2026-03-06T11:02:23Z
dc.date.issued2025-09-21
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in 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.abstractRetinal diseases are the leading cause of vision impairment and blindness worldwide. Early and accurate diagnosis is critical for effective treatment, and recent advances in imaging technologies such as Optical Coherence Tomography (OCT) and OCT Angiography (OCTA), have enabled detailed visualization of the retinal structure and vasculature. By leveraging these modalities, this study proposes an advanced deep learning architecture called MultiModalNet for automated multi-class retinal disease classification. MultiModalNet employs a dual-branch design, where OCTA projection maps are processed through a ResNet101 encoder, and cross-sectional slices from the OCT volume (B-scans) are analyzed using a Vision Transformer (ViT-Large). The extracted features from both branches were fused and passed through the fully connected layers for the final classification. Evaluated on the 3-class OCTA-500 dataset, which includes Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR), and Normal cases, the proposed model achieved state-of-the-art classification accuracy of 94.59 percent, significantly o utperforming single-modality baselines. This result highlights the effectiveness of integrating vascular and structural information to improve the diagnostic performance. The findings suggest that hybrid multi-modal deep learning approaches can play a transformative role in computer-aided ophthalmology, enhancing both clinical decision-making and screening workflows.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationAydın, Ö. F., Tek, F. B. & Turkan, Y. (2025). Retinal disease classification from bimodal OCT and OCTA using a CNN-ViT hybrid architecture. Paper presented at the International Conference on Computer Science and Engineering, UBMK, 2025, 260-264. doi:https://doi.org/10.1109/UBMK67458.2025.11206835en_US
dc.identifier.doi10.1109/UBMK67458.2025.11206835
dc.identifier.endpage264
dc.identifier.isbn2521-1641
dc.identifier.isbn9798331599751
dc.identifier.issue2025
dc.identifier.scopus2-s2.0-105030845081
dc.identifier.scopusqualityN/A
dc.identifier.startpage260
dc.identifier.urihttps://hdl.handle.net/11729/7105
dc.identifier.urihttps://doi.org/10.1109/UBMK67458.2025.11206835
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.ispartofInternational Conference on Computer Science and Engineering, UBMKen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.subjectDeep learningen_US
dc.subjectMulti-modalen_US
dc.subjectOptical Coherence Tomography Angiography (OCTA)en_US
dc.subjectRetinal disease classificationen_US
dc.subjectVision Transformer (ViT)en_US
dc.subjectArchitectureen_US
dc.subjectClassification (of information)en_US
dc.subjectComputer aided diagnosisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDecision makingen_US
dc.subjectDeep neural networksen_US
dc.subjectEye protectionen_US
dc.subjectLearning systemsen_US
dc.subjectOphthalmologyen_US
dc.subjectCoherence tomographyen_US
dc.subjectConvolutional neural networken_US
dc.subjectDisease classificationen_US
dc.subjectRetinal diseaseen_US
dc.subjectAngiographyen_US
dc.titleRetinal disease classification from bimodal OCT and OCTA using a CNN-ViT hybrid architectureen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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