Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77331
DC FieldValueLanguage
dc.contributor.authorUteng, Stigen_US
dc.contributor.authorQuevedo Gutiérrez, Eduardo Gregorioen_US
dc.contributor.authorMarrero Callicó, Gustavo Ivánen_US
dc.contributor.authorCastaño González, Ireneen_US
dc.contributor.authorCarretero Hernández, Gregorioen_US
dc.contributor.authorAlmeida Martín, Pabloen_US
dc.contributor.authorGarcia del Toro, Adayen_US
dc.contributor.authorHernández Santana, Javier A.en_US
dc.contributor.authorGodtliebsen, Freden_US
dc.date.accessioned2021-01-26T09:19:16Z-
dc.date.available2021-01-26T09:19:16Z-
dc.date.issued2021en_US
dc.identifier.issn1424-8220en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/77331-
dc.description.abstractThis paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.en_US
dc.languageengen_US
dc.relation.ispartofSensors (Switzerland)en_US
dc.sourceSensors (Switzerland) [ISSN 1424-8220], v. 21 (3), p. 1-13en_US
dc.subject3314 Tecnología médicaen_US
dc.subject320106 Dermatologíaen_US
dc.subject.otherHyperspectralen_US
dc.subject.otherCurve fiten_US
dc.subject.otherStatistical discriminationen_US
dc.subject.otherMelanomaen_US
dc.subject.otherBenignen_US
dc.subject.otherMalignanten_US
dc.titleCurve‐based classification approach for hyperspectral dermatologic data processingen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s21030680en_US
dc.identifier.scopus85099677035-
dc.contributor.authorscopusid57216457386-
dc.contributor.authorscopusid55845740700-
dc.contributor.authorscopusid56006321500-
dc.contributor.authorscopusid57214689598-
dc.contributor.authorscopusid6506191408-
dc.contributor.authorscopusid8524503900-
dc.contributor.authorscopusid55452183800-
dc.contributor.authorscopusid57214748954-
dc.contributor.authorscopusid55974798000-
dc.identifier.issue3-
dc.relation.volume21en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.description.numberofpages13en_US
dc.utils.revisionen_US
dc.date.coverdateFebrero 2021en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,803
dc.description.jcr3,847
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.miaricds10,8
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Matemáticas-
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.orcid0000-0002-5415-3446-
crisitem.author.orcid0000-0002-3784-5504-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.fullNameQuevedo Gutiérrez, Eduardo Gregorio-
crisitem.author.fullNameMarrero Callicó, Gustavo Iván-
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