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dc.contributor.authorRey Barroso, Laura
dc.contributor.authorPeña Gutiérrez, Sara
dc.contributor.authorYáñez Alvarado, Carlos René
dc.contributor.authorBurgos Fernández, Francisco Javier
dc.contributor.authorVilaseca Ricart, Meritxell
dc.contributor.authorRoyo Royo, Santiago
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Òptica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Òptica i Optometria
dc.date.accessioned2021-01-18T11:01:42Z
dc.date.available2021-01-18T11:01:42Z
dc.date.issued2021-01-02
dc.identifier.citationRey, L. [et al.]. Optical technologies for the improvement of skin cancer diagnosis: a review. "Sensors", 2 Gener 2021, vol. 21, núm. 1, p. 1-31.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2117/335446
dc.description.abstractThe worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, threedimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.
dc.description.sponsorshipThis research was supported by the Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (FEDER) under the grants DPI2017-89414-R and FIS2017-89850-R. L.R-B. thanks the Spanish Government for the predoctoral FPI grant she received, grant number DPI2017-89414-R. S. P-G. thanks AGAUR for the FI grant she received, grant number 2020FI_B2 00068. C.Y. thanks CONACYT Mexico for the grant he received, grant number 472102
dc.format.extent31 p.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subjectÀrees temàtiques de la UPC::Ciències de la visió
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshSkin--Cancer
dc.subject.lcshInterferometry
dc.subject.lcshMachine learning
dc.subject.lcshCancer--Diagnosis
dc.subject.otherSkin cancer
dc.subject.otherMelanoma
dc.subject.otherMultispectral imaging
dc.subject.other3D topography
dc.subject.otherOptical feed-back interferometry
dc.subject.otherConfocal microscopy
dc.subject.otherOptical coherence tomography
dc.subject.otherPolarimetry
dc.subject.otherMachine learning
dc.titleOptical technologies for the improvement of skin cancer diagnosis: a review
dc.typeArticle
dc.subject.lemacPell -- Càncer
dc.subject.lemacInterferometria
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacCàncer -- Diagnòstic
dc.contributor.groupUniversitat Politècnica de Catalunya. GREO - Grup de Recerca en Enginyeria Òptica
dc.identifier.doi10.3390/s21010252
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/1/252
dc.rights.accessOpen Access
local.identifier.drac30145255
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/2PE/ DPI2017-89414-R
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/ FIS2017-89850-R
local.citation.authorRey, L.; Peña-Gutiérrez, S.; Yañez, C.; Burgos, Francisco J.; Vilaseca, M.; Royo, S.
local.citation.publicationNameSensors
local.citation.volume21
local.citation.number1
local.citation.startingPage1
local.citation.endingPage31


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Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain