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dc.contributor.authorMolina-Cabello, Miguel Ángel 
dc.contributor.authorRodríguez Rodríguez, José Antonio
dc.contributor.authorThurnhofer Hemsi, Karl
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2021-07-23T11:32:54Z
dc.date.available2021-07-23T11:32:54Z
dc.date.issued2021-07
dc.identifier.urihttps://hdl.handle.net/10630/22693
dc.description.abstractOne of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer aided diagnosis by intelligent image analysis can help in the diagnosis tasks associated with this disease. Here we propose an automated system for histopathological image analysis that is based on deep learning neural networks with convolutional layers. Rather than a single network, an ensemble of them is built so as to attain higher recognition rates, which are obtained by computing a consensus decision from the individual networks of the ensemble. A final step involves the optimization of the set of networks that are included in the ensemble by a genetic algorithm. Experimental results are provided with a set of benchmark images, with favorable outcomes.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMamas - Cánceres_ES
dc.subject.otherConvolutional neural networkses_ES
dc.subject.otherImage classificationes_ES
dc.subject.otherBreast canceres_ES
dc.subject.otherMedical image processinges_ES
dc.titleHistopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithmses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Joint Conference on Neural Networks 2021 (IJCNN 2021)es_ES
dc.relation.eventplaceVirtuales_ES
dc.relation.eventdateJulio de 2021es_ES


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