COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles[Formula presented]

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBreve, Fabricio Aparecido-
Data de aceite: dc.date.accessioned2025-08-21T21:56:21Z-
Data de disponibilização: dc.date.available2025-08-21T21:56:21Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2022-10-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2022.117549-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241900-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241900-
Descrição: dc.descriptionCOVID-19 quickly became a global pandemic after only four months of its first detection. It is crucial to detect this disease as soon as possible to decrease its spread. The use of chest X-ray (CXR) images became an effective screening strategy, complementary to the reverse transcription-polymerase chain reaction (RT-PCR). Convolutional neural networks (CNNs) are often used for automatic image classification and they can be very useful in CXR diagnostics. In this paper, 21 different CNN architectures are tested and compared in the task of identifying COVID-19 in CXR images. They were applied to the COVIDx8B dataset, a large COVID-19 dataset with 16,352 CXR images coming from patients of at least 51 countries. Ensembles of CNNs were also employed and they showed better efficacy than individual instances. The best individual CNN instance results were achieved by DenseNet169, with an accuracy of 98.15% and an F1 score of 98.12%. These were further increased to 99.25% and 99.24%, respectively, through an ensemble with five instances of DenseNet169. These results are higher than those obtained in recent works using the same dataset.-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP) Júlio de Mesquita Filho, Rio Claro-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP) Júlio de Mesquita Filho, Rio Claro-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems with Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectChest X-ray images-
Palavras-chave: dc.subjectConvolutional neural networks-
Palavras-chave: dc.subjectTransfer learning-
Título: dc.titleCOVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles[Formula presented]-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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