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dc.contributor.authorDuru, Dilek Göksel
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorAl-azzaw, Athar
dc.contributor.authorAl-jumaili, Saif
dc.date.accessioned2024-04-04T21:06:26Z
dc.date.available2024-04-04T21:06:26Z
dc.date.issued2023en_US
dc.identifier.citationDuru, Dilek G., Duru, Adil D., Uçan, Osman N., Al-azzaw, A., Al-jumaili, S. (2023). Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images. International Information and Engineering Technology Association, 40 (2), 407-420.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1039
dc.description.abstractIn 2019, the world had been attacked with a severe situation by the new version of the SARSCOV-2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew’s correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID19.en_US
dc.language.isoengen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.isversionof10.18280/ts.400201en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectClassificationen_US
dc.subjectCNNen_US
dc.subjectX-rayen_US
dc.subjectCT scanen_US
dc.titleEvaluation of deep transfer learning methodologies on the COVID-19 radiographic chest imagesen_US
dc.typearticleen_US
dc.identifier.volume40en_US
dc.identifier.issue2en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.departmentTAÜ, Fen Fakültesi, Moleküler Biyoteknoloji Bölümüen_US
dc.identifier.startpage407en_US
dc.identifier.endpage420en_US


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