Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1927
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dc.contributor299983es_ES
dc.contributor326164es_ES
dc.contributor266942es_ES
dc.contributor268446es_ES
dc.contributor49237es_ES
dc.contributor.other0000-0002-7635-4687es_ES
dc.contributor.other0000-0002-9498-6602-
dc.contributor.otherhttps://orcid.org/0000-0002-9498-6602-
dc.coverage.spatialGlobales_ES
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorZanella Calzada, Laura Alejandra-
dc.creatorGalván Tejada, Jorge Issac-
dc.creatorCelaya Padilla, José María-
dc.creatorGamboa Rosales, Hamurabi-
dc.creatorGarza Veloz, Idalia-
dc.creatorMartínez Fierro, Margarita de la Luz-
dc.date.accessioned2020-05-21T18:37:30Z-
dc.date.available2020-05-21T18:37:30Z-
dc.date.issued2017-03-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn2075-4418es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1927-
dc.description.abstractBreast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationhttps://www.mdpi.com/2075-4418/7/1/9es_ES
dc.relation.urigeneralPublices_ES
dc.sourceDiagnostics Vol. 7, No. 1, pp. 1-17es_ES
dc.subject.classificationMEDICINA Y CIENCIAS DE LA SALUD [3]es_ES
dc.subject.otherBreast canceres_ES
dc.subject.othermammography image featureses_ES
dc.subject.othermammography descriptorses_ES
dc.subject.otherCADes_ES
dc.subject.othermultivariate modeles_ES
dc.subject.othergenetic algorithmes_ES
dc.subject.othermachine learning algorithmses_ES
dc.titleMultivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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