Investigation of the role of convolutional neural network architectures in the diagnosis of glaucoma using color fundus photography

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dc.authorid0000-0001-7749-2814en_US
dc.contributor.authorAtalay, Eray
dc.contributor.authorÖzalp, Onur
dc.contributor.authorDevecioğlu, Özer Can
dc.contributor.authorErdoğan, Hakika
dc.contributor.authorİnce, Türker
dc.contributor.authorYıldırım, Nilgün
dc.date.accessioned2024-07-12T21:11:49Z
dc.date.available2024-07-12T21:11:49Z
dc.date.issued2022en_US
dc.departmentFakülteler, Tıp Fakültesien_US
dc.description.abstractObjectives: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes. Materials and Methods: A total of 9,950 fundus photographs of 5,388 patients from the database of Eskişehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs. Results: Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4%, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.en_US
dc.identifier.citationAtalay, E., Özalp, O., Erdoğan, H. and at all. (2022). Investigation of the role of convolutional neural network architectures in the diagnosis of glaucoma using color fundus photography. Turk J Ophthalmol, 52, p.193-200.en_US
dc.identifier.doi10.4274/tjo.galenos.2021.29726
dc.identifier.endpage200en_US
dc.identifier.pmid35770344en_US
dc.identifier.scopus2-s2.0-85133145175en_US
dc.identifier.startpage193en_US
dc.identifier.trdizinid1175202en_US
dc.identifier.urihttps://doi.prg/10.4274/tjo.galenos.2021.29726
dc.identifier.urihttps://hdl.handle.net/20.500.12415/4413
dc.identifier.volume52en_US
dc.identifier.wosWOS:000821679600008en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.institutionauthorErdoğan, Hakika
dc.language.isoenen_US
dc.publisherTurk J Ophthalmolen_US
dc.relation.ispartofTurk J Ophthalmolen_US
dc.relation.publicationcategoryUlusal Hakemli Dergide Makale - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY03649
dc.subjectGlaucomaen_US
dc.subjectConvolutional neural networken_US
dc.subjectArtificial intelligenceen_US
dc.subjectTelemedicineen_US
dc.titleInvestigation of the role of convolutional neural network architectures in the diagnosis of glaucoma using color fundus photographyen_US
dc.typeArticle
dspace.entity.typePublication

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