An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

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dc.contributor.authorMiguel, Pedro Lucas [UNESP]
dc.contributor.authorCansian, Adriano Mauro [UNESP]
dc.contributor.authorRozendo, Guilherme Botazzo [UNESP]
dc.contributor.authorMedalha, Giuliano Cardozo
dc.contributor.authordo Nascimento, Marcelo Zanchetta
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionWZTECH NETWORKS
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.date.accessioned2023-07-29T13:57:25Z
dc.date.available2023-07-29T13:57:25Z
dc.date.issued2023-01-01
dc.description.abstractIn this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images.en
dc.description.affiliationDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP
dc.description.affiliationWZTECH NETWORKS, Avenida Romeu Strazzi (room 503-B), 325, SP
dc.description.affiliationFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, MG
dc.description.affiliationUnespDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP
dc.description.sponsorshipFaculdade de Medicina de São José do Rio Preto
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipIdCNPq: #120993/2020-1
dc.description.sponsorshipIdCNPq: #311404/2021-9
dc.description.sponsorshipIdCNPq: #313643/2021-0
dc.description.sponsorshipIdFAPEMIG: #APQ-00578-18
dc.format.extent675-682
dc.identifierhttp://dx.doi.org/10.5220/0012038500003467
dc.identifier.citationInternational Conference on Enterprise Information Systems, ICEIS - Proceedings, v. 1, p. 675-682.
dc.identifier.doi10.5220/0012038500003467
dc.identifier.issn2184-4992
dc.identifier.scopus2-s2.0-85160769624
dc.identifier.urihttp://hdl.handle.net/11449/248922
dc.language.isoeng
dc.relation.ispartofInternational Conference on Enterprise Information Systems, ICEIS - Proceedings
dc.sourceScopus
dc.subjectConvolutional Neural Networks
dc.subjectCOVID-19
dc.subjectDeep-Learned Features
dc.subjectRadiographic Images
dc.subjectRelieF
dc.titleAn Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19en
dc.typeTrabalho apresentado em evento
unesp.author.orcid0000-0003-4494-1454[2]
unesp.author.orcid0000-0003-3537-0178[5]
unesp.author.orcid0000-0001-8580-7054[6]

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