Repository logo
 
Publication

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images

2023Journal article Open access
Simple item page
dc.contributor.authorHayat, Ahatsham
dc.contributor.authorBaglat, Preety
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-16T09:43:35Z
dc.date.available2024-02-16T09:43:35Z
dc.date.issued2023
dc.description.abstractThe number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationHayat, A., Baglat, P., Mendonça, F., Mostafa, S. S., & Dias, F. M. (2023). Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images. International Journal of Environmental Research and Public Health, 20(2), 1268.pt_PT
dc.identifier.doi10.3390/ijerph20021268pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5556
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCOVID-19pt_PT
dc.subjectCT scanpt_PT
dc.subjectChest X-raypt_PT
dc.subjectMachine learningpt_PT
dc.subjectDeep learningpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleNovel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Imagespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage1268pt_PT
oaire.citation.titleInternational Journal of Environmental Research and Public Healthpt_PT
oaire.citation.volume20pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBaglat
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameAhatsham
person.givenNamePreety
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier2100796
person.identifier2616642
person.identifier34497
person.identifier.ciencia-id1711-4988-4E41
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0003-2000-5557
person.identifier.orcid0000-0002-3348-262X
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication1bf77d6f-5a7e-4815-94f6-b4fde83dd7f2
relation.isAuthorOfPublication82529790-b282-4dca-815a-8de72fcc03e6
relation.isAuthorOfPublication0dbad4bf-3a0c-4c43-8022-42199a5e09c0
relation.isAuthorOfPublicationf90aafd0-eedb-47ea-945a-40b1c1fe802a
relation.isAuthorOfPublication042f7593-c6ca-4553-8f0e-12ccf17018be
relation.isAuthorOfPublication.latestForDiscovery1bf77d6f-5a7e-4815-94f6-b4fde83dd7f2
relation.isProjectOfPublicationc0352e81-99e6-4923-99ed-074df09e4db0
relation.isProjectOfPublication.latestForDiscoveryc0352e81-99e6-4923-99ed-074df09e4db0

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.pdf
Size:
6.41 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Artigos em revistas internacionais