Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/58289
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dc.contributor.authorJohansen, Thomas Hauglanden_US
dc.contributor.authorMøllersen, Kajsaen_US
dc.contributor.authorOrtega Sarmiento, Samuelen_US
dc.contributor.authorFabelo Gómez, Himar Antonioen_US
dc.contributor.authorGarcía Romero, Leví Adayen_US
dc.contributor.authorMarrero Callicó, Gustavo Ivánen_US
dc.contributor.authorGodtliebsen, Freden_US
dc.date.accessioned2019-12-09T18:57:52Z-
dc.date.available2019-12-09T18:57:52Z-
dc.date.issued2020en_US
dc.identifier.issn1939-5108en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/58289-
dc.description.abstractSkin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer-based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high-quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale-space theory, machine learning, and so on, are essential. This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics.en_US
dc.languageengen_US
dc.relationIdentificación Hiperespectral de Tumores Cerebrales (Ithaca)en_US
dc.relation.ispartofWiley Interdisciplinary Reviews: Computational Statisticsen_US
dc.sourceWiley interdisciplinary reviews. Computational statistics [ISSN 1939-0068], v. 12(1), e1465en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherHyperspectral imagingen_US
dc.subject.otherMachine learningen_US
dc.subject.otherMelanomaen_US
dc.subject.otherSkin canceren_US
dc.titleRecent advances in hyperspectral imaging for melanoma detectionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/wics.1465en_US
dc.identifier.scopus85064664465-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
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dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid57208397186-
dc.contributor.authorscopusid36696635400-
dc.contributor.authorscopusid57189334144-
dc.contributor.authorscopusid56405568500-
dc.contributor.authorscopusid55452183800-
dc.contributor.authorscopusid56006321500-
dc.contributor.authorscopusid55974798000-
dc.description.firstpagee1465en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.esciESCI
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptGIR IOCAG: Geografía, Medio Ambiente y Tecnologías de la Información Geográfica-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.orcid0000-0002-7519-954X-
crisitem.author.orcid0000-0002-9794-490X-
crisitem.author.orcid0000-0002-4985-9073-
crisitem.author.orcid0000-0002-3784-5504-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.fullNameOrtega Sarmiento,Samuel-
crisitem.author.fullNameFabelo Gómez, Himar Antonio-
crisitem.author.fullNameGarcía Romero, Leví Aday-
crisitem.author.fullNameMarrero Callicó, Gustavo Iván-
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