COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers

Basit Öğe Kaydı
dc.authorid0000-0003-1866-4721en_US
dc.contributor.authorYıldırım, Muhammed
dc.contributor.authorEroğlu, Orkun
dc.contributor.authorEroğlu,Yeşim
dc.contributor.authorÇınar, Ahmet
dc.contributor.authorCengil, Emine
dc.date.accessioned2022-06-07T13:33:32Z
dc.date.available2022-06-07T13:33:32Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Mühendislik Temel Bilimleri Bölümüen_US
dc.description.abstractCoronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artifcial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and diferentiation from other viral pneumonia. The model we propose consists of three steps. In the frst step, classifcation was made using the MobilenetV2, Efcientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efcientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classifed in the classic machine learning classifers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classifed in the classifers. The model we proposed was tested on two diferent data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful.en_US
dc.identifier.citationYildirim, M., Eroğlu, O., Eroğlu, Y., Çinar, A., & Cengil, E. (2022). COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers. New Generation Computing, 1-15.en_US
dc.identifier.doi10.1007/s00354-022-00172-4
dc.identifier.endpage15en_US
dc.identifier.issn1882-7055en_US
dc.identifier.issn0288-3635en_US
dc.identifier.scopus2-s2.0-85129493808en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00354-022-00172-4
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1127
dc.identifier.wosWOS:000791641000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldırım, Muhammed
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNew Generation Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtifcial Intelligenceen_US
dc.subjectClassifcationen_US
dc.subjectDeep Learningen_US
dc.subjectNCAen_US
dc.subjectX-ray Imagesen_US
dc.titleCOVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiersen_US
dc.typeArticleen_US

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