A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

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dc.contributor.authorAkgun, Devrim
dc.contributor.authorKabakus, Abdullah Talha
dc.contributor.authorSenturk, Zehra Karapinar
dc.contributor.authorSenturk, Arafat
dc.contributor.authorKucukkulahli, Enver
dc.date.accessioned2021-12-01T18:47:47Z
dc.date.available2021-12-01T18:47:47Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThe COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study. The proposed models employed various pretrained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer learning technique, provided the best accuracy, 86.42%, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.en_US
dc.identifier.doi10.3906/elk-2105-64
dc.identifier.endpage2823en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.scopus2-s2.0-85117232219en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2807en_US
dc.identifier.urihttps://doi.org/10.3906/elk-2105-64
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10374
dc.identifier.volume29en_US
dc.identifier.wosWOS:000709712800003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectdiagnosticsen_US
dc.subjectaudio analysisen_US
dc.subjecttransfer learningen_US
dc.subjectconvolutional neural networken_US
dc.subjectdeep neural networken_US
dc.subjectConvolutional Neural-Networksen_US
dc.subjectClassificationen_US
dc.titleA transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio dataen_US
dc.typeArticleen_US

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