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dc.contributor.authorPatnaik, Sohan
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-13T10:36:37Z
dc.date.available2023-03-13T10:36:37Z
dc.date.issued2023-01-20
dc.identifier.citationSohan Patnaik, Abdulhamit Subasi, Chapter 12 - Artificial intelligence-based retinal disease classification using optical coherence tomography images, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 305-319, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00009-8. (https://www.sciencedirect.com/science/article/pii/B9780443184505000098)en_US
dc.identifier.isbn9780443184505en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-443-18450-5.00009-8en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/631
dc.description.abstractOptical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to aid in the early detection and diagnosis of retinal diseases and conditions. Notwithstanding the proven utility of OCT images, diagnosing large datasets of OCT images using the manual method still remains a challenge. In this chapter, we propose a deep learning-based approach, namely, the use of convolutional neural networks (CNN) and some pretrained image classification models on top of CNNs to get a proper and faster diagnosis of the OCT images. We also experiment with the features extracted using pretrained image classification models. Mainly three diseases—drusen, diabetic macular edema, choroidal neovascularization are addressed in this study. Our technique achieves an accuracy score of 0.9948 and an F1 score of 0.9948 on the test set. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when retinal diseases are suspected.en_US
dc.publisherAcademic Pressen_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectOptical Coherence Tomography (OCT)en_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.subjectDeep Transfer Learningen_US
dc.subjectDeep Feature Extractionen_US
dc.titleArtificial intelligence-based retinal disease classification using optical coherence tomography imagesen_US
dc.source.booktitleApplications of Artificial Intelligence in Medical Imaging.en_US
dc.source.volumeArtificial Intelligence Applications in Healthcare&Medicine;
dc.source.pages305-319en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorPatnaik, Sohan


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