A Hermite polynomial algorithm for detection of lesions in lymphoma images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro (IFTM)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorNeves, Leandro A. [UNESP]-
Autor(es): dc.creatorde Faria, Paulo R.-
Autor(es): dc.creatorTosta, Thaína A. A.-
Autor(es): dc.creatorLongo, Leonardo C. [UNESP]-
Autor(es): dc.creatorSilva, Adriano B.-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatordo Nascimento, Marcelo Z.-
Data de aceite: dc.date.accessioned2022-02-22T00:48:22Z-
Data de disponibilização: dc.date.available2022-02-22T00:48:22Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s10044-020-00927-z-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206832-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206832-
Descrição: dc.descriptionThere are different types of lesions that can be investigated with the hematoxylin–eosin staining protocol. Lymphoma is a type of malignant disease which affects one of the highest white blood cell populations responsible for the immunological defence system. There are lymphoma sub-types that can have similar features, which make their diagnoses a difficult task. In this study, we investigated algorithms based on multiscale and multidimensional fractal geometry with colour models for classification of lymphoma images. Fractal features were extracted from the colour models and separate channels from these models. These features were concatenated to form feature vectors. Finally, we investigated the Hermite polynomial classifier and machine learning algorithms in order to evaluate the performance of the proposed approach. We employed the tenfold cross-validation method and evaluated the lesion sub-types with the binary and multiclass classifications. The separated colour channels obtained from histological images achieved relevant values for the binary and multiclass classifications, with an accuracy rating between 91 and 97%. These results can contribute to the detection and classification of the lesions by supporting specialists in clinical practices.-
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.descriptionFederal Institute of Triângulo Mineiro (IFTM), Rua Belarmino Vilela Junqueira sn-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Neves de Ávila 2121, Bl.B-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265-
Descrição: dc.descriptionCNPq: (#304848/2018-2)-
Descrição: dc.descriptionCNPq: (#313365/2018-0)-
Descrição: dc.descriptionCNPq: (#427114/2016-0)-
Descrição: dc.descriptionCNPq: (#430965/2018-4)-
Descrição: dc.descriptionFAPEMIG: (#APQ-00578-18)-
Formato: dc.format523-535-
Idioma: dc.languageen-
Relação: dc.relationPattern Analysis and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectColour fractal-
Palavras-chave: dc.subjectColour spaces-
Palavras-chave: dc.subjectHermite polynomial-
Palavras-chave: dc.subjectLymphoma-
Título: dc.titleA Hermite polynomial algorithm for detection of lesions in lymphoma images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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