Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

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dc.authorid0000-0002-2275-8133
dc.authorid0000-0001-6866-3095
dc.authorid0000-0001-8618-7256
dc.authorid0000-0001-5023-0961
dc.authorid0000-0002-8633-3505
dc.contributor.authorSheykhivand, Sobhanen_US
dc.contributor.authorMousavi, Zohrehen_US
dc.contributor.authorMojtahedi, Sinaen_US
dc.contributor.authorYousefi Rezaii, Tohiden_US
dc.contributor.authorFarzamnia, Alien_US
dc.contributor.authorMeshgini, Saeeden_US
dc.contributor.authorSaad, Ismailen_US
dc.date.accessioned2021-02-16T11:16:35Z
dc.date.available2021-02-16T11:16:35Z
dc.date.issued2021-06
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.description.abstractThe novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID-19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationSheykhivand, S., Mousavi, Z., Mojtahedi, S., Yousefi Rezaii, T., Farzamnia, A., Meshgini, S. & Saad, I. (2021). Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images. Alexandria Engineering Journal, 60(3), 2885-2903. doi: 10.1016/j.aej.2021.01.011en_US
dc.identifier.doi10.1016/j.aej.2021.01.011
dc.identifier.endpage2903
dc.identifier.issn1110-0168
dc.identifier.issn2090-2670
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85100426991
dc.identifier.scopusqualityQ1
dc.identifier.startpage2885
dc.identifier.urihttps://hdl.handle.net/11729/3096
dc.identifier.urihttp://dx.doi.org/10.1016/j.aej.2021.01.011
dc.identifier.volume60
dc.identifier.wosWOS:000634505700015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorMojtahedi, Sinaen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAlexandria Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNNen_US
dc.subjectCOVID-19en_US
dc.subjectGANsen_US
dc.subjectLSTMen_US
dc.subjectPneumoniaen_US
dc.subjectTransfer learningen_US
dc.subjectX-ray Imagesen_US
dc.subjectAutomationen_US
dc.subjectDeep learningen_US
dc.subjectDeep neural networksen_US
dc.subjectDiagnosisen_US
dc.subjectDisease controlen_US
dc.subjectPatient treatmenten_US
dc.subjectAdversarial networksen_US
dc.subjectAutomatic detectionen_US
dc.subjectChest X-ray imageen_US
dc.subjectCoronavirusesen_US
dc.subjectFeature extraction/selectionen_US
dc.subjectFunctional scenariosen_US
dc.subjectLearning approachen_US
dc.subjectProposed architecturesen_US
dc.subjectLong short-term memoryen_US
dc.titleDeveloping an efficient deep neural network for automatic detection of COVID-19 using chest X-ray imagesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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