Lightweight neural architectures to improve COVID-19 identification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorKing Saud University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorHassan, Mohammad Mehedi-
Autor(es): dc.creatorAlQahtani, Salman A.-
Autor(es): dc.creatorAlelaiwi, Abdulhameed-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2025-08-21T20:06:42Z-
Data de disponibilização: dc.date.available2025-08-21T20:06:42Z-
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/10.3389/fphy.2023.1153637-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249849-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249849-
Descrição: dc.descriptionThe COVID-19 pandemic has had a global impact, transforming how we manage infectious diseases and interact socially. Researchers from various fields have worked tirelessly to develop vaccines on an unprecedented scale, while different countries have developed various sanitary protocols to deal with more contagious variants. Machine learning-assisted diagnosis has emerged as a powerful tool that can help health professionals deliver faster and more accurate outcomes. However, medical systems that rely on deep learning often require extensive data, which may be impractical for real-world applications. This paper compares lightweight neural architectures for COVID-19 identification using chest X-rays, highlighting the strengths and weaknesses of each approach. Additionally, a web tool has been developed that accepts chest computer tomography images and outputs the probability of COVID-19 infection along with a heatmap of the regions used by the intelligent system to make this determination. The experiments indicate that most lightweight architectures considered in the study can identify COVID-19 correctly, but further investigation is necessary. Lightweight neural architectures show promise in computer-aided COVID-19 diagnosis using chest X-rays, but they did not reach accuracy rates above 88%, which is necessary for medical applications. These findings suggest that additional research is necessary to improve the accuracy of lightweight models and make them practical for real-world use.-
Descrição: dc.descriptionKing Abdulaziz City for Science and Technology-
Descrição: dc.descriptionCollege of Computer and Information Sciences King Saud University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Physics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectconvolutional neural networks-
Palavras-chave: dc.subjectCOVID-19-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectheatmap analyses-
Palavras-chave: dc.subjectweb tool-
Título: dc.titleLightweight neural architectures to improve COVID-19 identification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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