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dc.contributor.authorViloria, Amelec
dc.contributor.authorVarela, Noel
dc.contributor.authorNuñez-Bravo, Narledys
dc.contributor.authorPineda Lezama, Omar Bonerge
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
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.typeArtículo de revistaspa
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    Artículos de investigación publicados por miembros de la comunidad universitaria.

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Attribution-NonCommercial-NoDerivatives 4.0 International
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