Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images

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dc.contributor.authorAl-Azzawi, Athar
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorDuru, Dilek Göksel
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2023-07-03T13:47:10Z
dc.date.available2023-07-03T13:47:10Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
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 COVID- 19.en_US
dc.identifier.citationAl-azzawi, A., Al-jumaili, S., Duru, A. D., Duru, D. G., & Uçan, O. N. (2023). Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images. Traitement du Signal, 40(2), 407-420.en_US
dc.identifier.endpage420en_US
dc.identifier.issn0765-0019
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85162086251
dc.identifier.scopusqualityN/A
dc.identifier.startpage407en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3554
dc.identifier.volume40en_US
dc.identifier.wosWOS:000996210200001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Azzawi, Athar
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signal
dc.relation.isversionof10.18280/ts.400201en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectCNNen_US
dc.subjectCT Scanen_US
dc.subjectDeep Learningen_US
dc.subjectDeep Transfer Learningen_US
dc.subjectX-Rayen_US
dc.titleEvaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images
dc.typeArticle

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