CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection

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dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorUnlersen, Muhammed Fahri
dc.contributor.authorSabanci, Kadir
dc.contributor.authorDurdu, Akif
dc.date.accessioned2024-02-23T14:02:10Z
dc.date.available2024-02-23T14:02:10Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractCoronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipRAC-LAB, Turkeyen_US
dc.description.sponsorshipAuthors are grateful to the RAC-LAB, Turkey (www.rac-lab.com) for training and support.en_US
dc.identifier.doi10.1016/j.asoc.2020.106912
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.pmid33230395en_US
dc.identifier.scopus2-s2.0-85096902763en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106912
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11618
dc.identifier.volume98en_US
dc.identifier.wosWOS:000603366000002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlexneten_US
dc.subjectBilstmen_US
dc.subjectCovid-19en_US
dc.subjectHybrid Architectureen_US
dc.subjectTransfer Learningen_US
dc.titleCNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detectionen_US
dc.typeArticleen_US

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