MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authorid, Ahmet Alp SARICA/0009-0005-7362-1291
dc.authorwosiderdem, oğuzhan/AAG-6229-2019
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorSarica, Ahmet Alp
dc.contributor.authorErdem, Oguzhan
dc.contributor.authorKaraali, Ali
dc.date.accessioned2024-06-12T10:58:37Z
dc.date.available2024-06-12T10:58:37Z
dc.date.issued2023
dc.departmentTrakya Üniversitesien_US
dc.description.abstractCoronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes Mel Frequency Cepstral Coefficients (MFCC), Spectrogram, and Chromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets.en_US
dc.description.sponsorshipDepartment of Nephrology, St. James's Hospital, Dublin Irelanden_US
dc.description.sponsorshipAli Karaali is partly funded by Department of Nephrology, St. James's Hospital, Dublin Ireland.en_US
dc.identifier.doi10.1007/s11517-023-02803-4
dc.identifier.endpage1629en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue7en_US
dc.identifier.pmid36828944en_US
dc.identifier.scopus2-s2.0-85148656126en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1619en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-023-02803-4
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20136
dc.identifier.volume61en_US
dc.identifier.wosWOS:000939923300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoronavirusen_US
dc.subjectCoughingen_US
dc.subjectDeep Learningen_US
dc.subjectEnsemble Learningen_US
dc.subjectTelehealthen_US
dc.subjectDiagnosisen_US
dc.titleMSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough soundsen_US
dc.typeArticleen_US

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