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dc.contributor.authorGhosh, Sarada
dc.contributor.authorSamanta, Guruprasad
dc.contributor.authorDe la Sen Parte, Manuel ORCID
dc.date.accessioned2021-05-26T11:26:08Z
dc.date.available2021-05-26T11:26:08Z
dc.date.issued2021-05-18
dc.identifier.citationComputation 9(5) : (2021) // Article ID 59es_ES
dc.identifier.issn2079-3197
dc.identifier.urihttp://hdl.handle.net/10810/51634
dc.description.abstractBreast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.es_ES
dc.description.sponsorshipThis research was funded by the Spanish Government for its support through grant RTI2018-094336-B-100 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MCIU/RTI2018-094336-B-100es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectbreast canceres_ES
dc.subjectmammographyes_ES
dc.subjectclassificationes_ES
dc.subjectmulti-model ensemblees_ES
dc.subjectFuzzy c-meanses_ES
dc.titleMulti-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2021-05-24T15:04:45Z
dc.rights.holder2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2079-3197/9/5/59/htmes_ES
dc.identifier.doi10.3390/computation9050059
dc.departamentoesElectricidad y electrónica
dc.departamentoeuElektrizitatea eta elektronika


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2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).