A Hybrid Approach for Breast Mass Categorization

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.creatorPassos, Leandro Aparecido [UNESP]-
Autor(es): dc.creatorSantos, Claudio-
Autor(es): dc.creatorPereira, Clayton Reginaldo [UNESP]-
Autor(es): dc.creatorAfonso, Luis Claudio Sugi-
Autor(es): dc.creatorPapa, João P. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:32:56Z-
Data de disponibilização: dc.date.available2022-02-22T00:32:56Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-32040-9_17-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201220-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201220-
Descrição: dc.descriptionBreast cancer is one of the most frequent fatal diseases among women around the world. Early diagnosis is paramount for easing such statistics, increasing the probability of successful treatment and cure. This paper proposes a hybrid approach composed of a convolutional neural network with a supervised classifier on the top capable of predicting eight specific cases of the breast tumor, being four of them malignant and four benign. The model employs the BreastNet convolution neural network to the task of mammogram images feature extraction, and it compares three distinct supervised-learning algorithms for classification purposes: (i) Optimum-Path Forest, (ii) Support Vector Machines (SVM) with Radial Basis Function, and (iii) SVM with a linear kernel. Moreover, since BreastNet is also capable of performing classification tasks, its results are further compared against the other three techniques. Experimental results demonstrate the robustness of the model, achieving 86 % of accuracy over the public LAPIMO dataset.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSchool of Sciences UNESP - São Paulo State University-
Descrição: dc.descriptionDepartment of Computing UFSCar - Federal University of São Carlos-
Descrição: dc.descriptionSchool of Sciences UNESP - São Paulo State University-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2014/12236-1-
Descrição: dc.descriptionFAPESP: 2016/19403-6-
Descrição: dc.descriptionCNPq: 307066/2017-7-
Descrição: dc.descriptionCNPq: 427968/2018-6-
Formato: dc.format159-168-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computational Vision and Biomechanics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBreast cancer-
Palavras-chave: dc.subjectConvolutional Neural Networks-
Palavras-chave: dc.subjectOptimum-path forest-
Título: dc.titleA Hybrid Approach for Breast Mass Categorization-
Aparece nas coleções:Repositório Institucional - Unesp

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