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

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorWZTECH NETWORKS-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorMiguel, Pedro Lucas-
Autor(es): dc.creatorCansian, Adriano Mauro-
Autor(es): dc.creatorRozendo, Guilherme Botazzo-
Autor(es): dc.creatorMedalha, Giuliano Cardozo-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Autor(es): dc.creatorNeves, Leandro Alves-
Data de aceite: dc.date.accessioned2025-08-21T20:27:23Z-
Data de disponibilização: dc.date.available2025-08-21T20:27:23Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0012038500003467-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248922-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248922-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionFaculdade de Medicina de São José do Rio Preto-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionWZTECH NETWORKS, Avenida Romeu Strazzi (room 503-B), 325, SP-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, MG-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionCNPq: #120993/2020-1-
Descrição: dc.descriptionCNPq: #311404/2021-9-
Descrição: dc.descriptionCNPq: #313643/2021-0-
Descrição: dc.descriptionFAPEMIG: #APQ-00578-18-
Formato: dc.format675-682-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Enterprise Information Systems, ICEIS - Proceedings-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional Neural Networks-
Palavras-chave: dc.subjectCOVID-19-
Palavras-chave: dc.subjectDeep-Learned Features-
Palavras-chave: dc.subjectRadiographic Images-
Palavras-chave: dc.subjectRelieF-
Título: dc.titleAn Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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