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dc.creatorViloria, Amelec
dc.creatorVarela, Noel
dc.creatorNuñez-Bravo, Narledys
dc.creatorPineda Lezama, Omar Bonerge
dc.date.accessioned2021-01-18T17:39:40Z
dc.date.available2021-01-18T17:39:40Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/11323/7708
dc.description.abstractDeep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, [2]). This image classification is very useful in the field of medicine, in which there is a growing interest in the use of data mining techniques in recent years. In this paper, a deep learning network was selected and trained for the analysis of a set of skin cancer data, obtaining very satisfactory results, as the model surpassed the classification results of trained dermatologists using a dermatoscope, other automatic learning techniques, and other deep learning techniques.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectDeep learningspa
dc.subjectMedical imagesspa
dc.subjectClinical data analysisspa
dc.titleMethod for the recovery of images in databases of skin cancerspa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_94spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-7234-0_94


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