Percolation Images: Fractal Geometry Features for Brain Tumor Classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Bologna-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro (IFTM)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorLumini, Alessandra-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatorNeves, Leandro Alves-
Autor(es): dc.creatorMartins, Alessandro Santana-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Data de aceite: dc.date.accessioned2025-08-21T21:00:50Z-
Data de disponibilização: dc.date.available2025-08-21T21:00:50Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-47606-8_29-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308182-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308182-
Descrição: dc.descriptionBrain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a “percolation” image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.-
Descrição: dc.descriptionDepartment of Computer Science and Engineering University of Bologna, FC-
Descrição: dc.descriptionInstitute of Mathematics and Computer Science (ICMC) University of São Paulo (USP), SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro (IFTM), MG-
Descrição: dc.descriptionFaculty of Computation (FACOM) Federal University of Uberlândia (UFU), MG-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), SP-
Formato: dc.format557-570-
Idioma: dc.languageen-
Relação: dc.relationAdvances in Neurobiology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBrain tumors-
Palavras-chave: dc.subjectClassification ensemble-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectFeature representations-
Palavras-chave: dc.subjectFractal features-
Título: dc.titlePercolation Images: Fractal Geometry Features for Brain Tumor Classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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