Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1454
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dc.contributor200970es_ES
dc.contributor241916es_ES
dc.contributor172879es_ES
dc.contributor49237es_ES
dc.contributor268446es_ES
dc.coverage.spatialGlobales_ES
dc.creatorOrtíz Rodríguez, José Manuel-
dc.creatorGuerrero Méndez, Carlos-
dc.creatorMartínez Blanco, María del Rosario-
dc.creatorCastro Tapia, Salvador-
dc.creatorMoreno Lucio, Mireya-
dc.creatorJaramillo Martínez, Ramón-
dc.creatorSolís Sánchez, Luis Octavio-
dc.creatorMartínez Fierro, Margarita de la Luz-
dc.creatorGarza Veloz, Idalia-
dc.creatorMoreira Galván, José Cruz-
dc.creatorBarrios García, Jorge Alberto-
dc.date.accessioned2020-03-24T20:19:48Z-
dc.date.available2020-03-24T20:19:48Z-
dc.date.issued2017-12-20-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.isbn978-953-51-3781-8es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1454-
dc.description.abstractBreast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed.es_ES
dc.language.isoenges_ES
dc.publisherIntechOpenes_ES
dc.relationhttp://dx.doi.org/10.5772/intechopen.71256es_ES
dc.relation.urigeneralPublices_ES
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.sourceAdvanced Applications for Artificial Neural Networks; Adel El-Shahat, coordinadora. p. 161-179es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherbreast cancer detectiones_ES
dc.subject.otherdigital image processinges_ES
dc.subject.otherartificial neural networkses_ES
dc.subject.otherbiomarkerses_ES
dc.subject.othercomputer-aided diagnosises_ES
dc.titleBreast Cancer Detection by Means of Artificial Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
Appears in Collections:*Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada

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