An Ensemble-based Approach for Breast Mass Classification in Mammography Images

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dc.contributor.authorRibeiro, Patricia B. [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorRomero, Roseli A. F.
dc.contributor.authorArmato, S. G.
dc.contributor.authorPetrick, N. A.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2018-11-26T17:39:56Z
dc.date.available2018-11-26T17:39:56Z
dc.date.issued2017-01-01
dc.description.abstractMammography analysis is an important tool that helps detecting breast cancer at the very early stages of the disease, thus increasing the quality of life of hundreds of thousands of patients worldwide. In Computer-Aided Detection systems, the identification of mammograms with and without masses (without clinical findings) is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest that may contain some suspicious content. In this work, the introduce a variant of the Optimum-Path Forest (OPF) classifier for breast mass identification, as well as we employed an ensemble-based approach that can enhance the effectiveness of individual classifiers aiming at dealing with the aforementioned purpose. The experimental results also comprise the naIve OPF and a traditional neural network, being the most accurate results obtained through the ensemble of classifiers, with an accuracy nearly to 86%.en
dc.description.affiliationSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.affiliationUniv Sao Paulo, Dept Comp Sci, Sao Carlos, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.format.extent8
dc.identifierhttp://dx.doi.org/10.1117/12.2250083
dc.identifier.citationMedical Imaging 2017: Computer-aided Diagnosis. Bellingham: Spie-int Soc Optical Engineering, v. 10134, 8 p., 2017.
dc.identifier.doi10.1117/12.2250083
dc.identifier.fileWOS000406425300092.pdf
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11449/163059
dc.identifier.wosWOS:000406425300092
dc.language.isoeng
dc.publisherSpie-int Soc Optical Engineering
dc.relation.ispartofMedical Imaging 2017: Computer-aided Diagnosis
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.titleAn Ensemble-based Approach for Breast Mass Classification in Mammography Imagesen
dc.typeTrabalho apresentado em evento
dcterms.rightsHolderSpie-int Soc Optical Engineering
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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