Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms

The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of a...

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Autores: Lati, Abdelhai|||0000-0002-5388-882X, Bensid, Khaled|||0000-0001-8502-907X, Lati, Ibtissem, Gezzal, Chahra
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:283778
Acceso en línea:https://ddd.uab.cat/record/283778
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1601
Access Level:acceso abierto
Palabra clave:COVID-19
Support Vector Machine (SVM)
VGG19
AlexNet
ResNet50
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spelling Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithmsLati, Abdelhai|||0000-0002-5388-882XBensid, Khaled|||0000-0001-8502-907XLati, IbtissemGezzal, ChahraCOVID-19Support Vector Machine (SVM)VGG19AlexNetResNet50The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of applications. Since it has been proved that transfer learning is effective for the medical classification tasks, in this study; COVID -19 detection system is implemented as a quick alternative, accurate and reliable diagnosis option to detect COVID-19 disease. Three pre-trained convolutional neural network based models (ResNet50, VGG19, AlexNet) have been proposed for this system. Based on the obtained performance results, the pre-trained models with support vector machine (SVM) provide the best classification performance compared to the used models individually. 22023-01-0120232023-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/283778https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1601reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2837782026-06-06T12:50:31Z
dc.title.none.fl_str_mv Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
title Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
spellingShingle Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
Lati, Abdelhai|||0000-0002-5388-882X
COVID-19
Support Vector Machine (SVM)
VGG19
AlexNet
ResNet50
title_short Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
title_full Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
title_fullStr Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
title_full_unstemmed Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
title_sort Enhanced SVM based Covid 19 detection system using efficient transfer learning algorithms
dc.creator.none.fl_str_mv Lati, Abdelhai|||0000-0002-5388-882X
Bensid, Khaled|||0000-0001-8502-907X
Lati, Ibtissem
Gezzal, Chahra
author Lati, Abdelhai|||0000-0002-5388-882X
author_facet Lati, Abdelhai|||0000-0002-5388-882X
Bensid, Khaled|||0000-0001-8502-907X
Lati, Ibtissem
Gezzal, Chahra
author_role author
author2 Bensid, Khaled|||0000-0001-8502-907X
Lati, Ibtissem
Gezzal, Chahra
author2_role author
author
author
dc.subject.none.fl_str_mv COVID-19
Support Vector Machine (SVM)
VGG19
AlexNet
ResNet50
topic COVID-19
Support Vector Machine (SVM)
VGG19
AlexNet
ResNet50
description The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of applications. Since it has been proved that transfer learning is effective for the medical classification tasks, in this study; COVID -19 detection system is implemented as a quick alternative, accurate and reliable diagnosis option to detect COVID-19 disease. Three pre-trained convolutional neural network based models (ResNet50, VGG19, AlexNet) have been proposed for this system. Based on the obtained performance results, the pre-trained models with support vector machine (SVM) provide the best classification performance compared to the used models individually.
publishDate 2023
dc.date.none.fl_str_mv 2
2023-01-01
2023
2023-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/283778
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1601
url https://ddd.uab.cat/record/283778
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1601
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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