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dc.contributor.authorLoey, M
dc.contributor.authorMirjalili, S
dc.date.accessioned2021-11-25T07:21:25Z
dc.date.available2021-11-25T07:21:25Z
dc.date.issued2021
dc.identifier.issn0010-4825
dc.identifier.doi10.1016/j.compbiomed.2021.105020
dc.identifier.urihttp://hdl.handle.net/10072/410354
dc.description.abstractDeep Learning shows promising performance in diverse fields and has become an emerging technology in Artificial Intelligence. Recent visual recognition is based on the ranking of photographs and the finding of artefacts in those images. The aim of this research is to classify the different cough sounds of COVID-19 artefacts in the signals of altered real-life environments. The introduced model takes into consideration two major steps. The first step is the transformation phase from sound to image that is optimized by the scalogram technique. The second step involves feature extraction and classification based on six deep transfer models (GoogleNet, ResNet18, ResNet50, ResNet101, MobileNetv2, and NasNetmobile). The dataset used contains 1457 (755 of COVID-19 and 702 of healthy) wave cough sounds. Although our recognition model performs the best, its accuracy only reaches 94.9% based on SGDM optimizer. The accuracy is promising enough for a wide set of labeled cough data to test the potential for generalization. The outcomes show that ResNet18 is the most stable model to classify the cough sounds from a limited dataset with a sensitivity of 94.44% and a specificity of 95.37%. Finally, a comparison of the research with a similar analysis is made. It is observed that the proposed model is more reliable and accurate than any current models. Cough research precision is promising enough to test the ability for extrapolation and generalization.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherElsevier BV
dc.relation.ispartofpagefrom105020
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.relation.ispartofvolume139
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode4602
dc.subject.keywordsCOVID-19
dc.subject.keywordsCough sound
dc.subject.keywordsDeep learning
dc.subject.keywordsScalogram images
dc.subject.keywordsSound classification
dc.titleCOVID-19 cough sound symptoms classification from scalogram image representation using deep learning models
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationLoey, M; Mirjalili, S, COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models, Computers in Biology and Medicine, 2021, 139, pp. 105020-
dcterms.dateAccepted2021-11-02
dc.date.updated2021-11-23T22:44:01Z
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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