BreastNet: Breast cancer categorization using convolutional neural networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSantos, Claudio-
Autor(es): dc.creatorAfonso, Luis-
Autor(es): dc.creatorPereira, Clayton [UNESP]-
Autor(es): dc.creatorPapa, Joao [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:43:40Z-
Data de disponibilização: dc.date.available2022-02-22T00:43:40Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/CBMS49503.2020.00094-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/205181-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/205181-
Descrição: dc.descriptionBreast cancer is usually classified as either benign or malignant, where the former is not considered hazardous to health. Nonetheless, the benign tumors must be periodically monitored to control their activity and to prevent them from becoming malignant eventually. Several automated techniques have been proposed to aid the diagnosis by indicating potential tumor locations or by providing a broader insight. Although benign and malignant tumors are divided into four categories each, most of the works cope with their classification as just benign and malignant. This work addresses the problem of providing a more detailed classification of the tumors by proposing a deep-based architecture able to distinguish between eight types of tumors (i.e., four benign and four malignant). The proposed approach relies on the fusion of traditional convolution kernels with dilated convolutions before pooling, which can learn better spatial information, thus providing better feature detection prior to classification. Experimental results showed that the proposed approach outperformed the techniques compared in this work.-
Descrição: dc.descriptionDepartment of Computing UFSCar - Federal University of São Carlos-
Descrição: dc.descriptionSchool of Sciences UNESP - São Paulo State University-
Descrição: dc.descriptionSchool of Sciences UNESP - São Paulo State University-
Formato: dc.format463-468-
Idioma: dc.languageen-
Relação: dc.relationProceedings - IEEE Symposium on Computer-Based Medical Systems-
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Palavras-chave: dc.subjectBreast cancer-
Palavras-chave: dc.subjectConvolutional neural networks-
Palavras-chave: dc.subjectDeep learning-
Título: dc.titleBreastNet: Breast cancer categorization using convolutional neural networks-
Aparece nas coleções:Repositório Institucional - Unesp

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