Exploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Porto (FEUP)-
Autor(es): dc.contributorScience and Technology of São Paulo (IFSP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversity of Bologna-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorRoberto, Guilherme F.-
Autor(es): dc.creatorPereira, Danilo C.-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorTosta, Thaína A.A.-
Autor(es): dc.creatorSoares, Carlos-
Autor(es): dc.creatorLumini, Alessandra-
Autor(es): dc.creatorRozendo, Guilherme B.-
Autor(es): dc.creatorNeves, Leandro A.-
Autor(es): dc.creatorNascimento, Marcelo Z.-
Data de aceite: dc.date.accessioned2025-08-21T20:46:20Z-
Data de disponibilização: dc.date.available2025-08-21T20:46:20Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patrec.2024.07.022-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309198-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309198-
Descrição: dc.descriptionCovid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)-
Descrição: dc.descriptionFaculty of Engineering University of Porto (FEUP)-
Descrição: dc.descriptionFederal Institute of Education Science and Technology of São Paulo (IFSP), SP-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP), SP-
Descrição: dc.descriptionDepartment of Computer Science and Engineering (DISI) University of Bologna, FC-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), MG-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), SP-
Descrição: dc.descriptionCNPq: #132940/2019-1-
Descrição: dc.descriptionFAPESP: #2022/03020-1-
Descrição: dc.descriptionCNPq: #311404/2021-9-
Descrição: dc.descriptionCNPq: #313643/2021-0-
Descrição: dc.descriptionFAPEMIG: #APQ-00578-18-
Descrição: dc.descriptionFAPEMIG: #APQ-01129-21-
Formato: dc.format248-255-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition Letters-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectChest X-ray images-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectCovid-19-
Palavras-chave: dc.subjectHandcrafted features-
Palavras-chave: dc.subjectPercolation-
Título: dc.titleExploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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