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dc.contributor.authorFanjul Vélez, Félix 
dc.contributor.authorPampín Suárez, Sandra
dc.contributor.authorArce Diego, José Luis 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2020-09-14T13:15:19Z
dc.date.available2020-09-14T13:15:19Z
dc.date.issued2020-07-03
dc.identifier.issn1099-4300
dc.identifier.otherPGC2018-101464-B-I00es_ES
dc.identifier.otherRTC-2015-4285-1es_ES
dc.identifier.urihttp://hdl.handle.net/10902/19106
dc.description.abstractBiological tissue identification in real clinical scenarios is a relevant and unsolved medical problem, particularly in the operating room. Although it could be thought that healthy tissue identification is an immediate task, in practice there are several clinical situations that greatly impede this process. For instance, it could be challenging in open surgery in complex areas, such as the neck, where different structures are quite close together, with bleeding and other artifacts affecting visual inspection. Solving this issue requires, on one hand, a high contrast noninvasive technique and, on the other hand, powerful classification algorithms. Regarding the technique, optical diffuse reflectance spectroscopy has demonstrated such capabilities in the discrimination of tumoral and healthy biological tissues. The complex signals obtained, in the form of spectra, need to be adequately computed in order to extract relevant information for discrimination. As usual, accurate discrimination relies on massive measurements, some of which serve as training sets for the classification algorithms. In this work, diffuse reflectance spectroscopy is proposed, implemented, and tested as a potential technique for healthy tissue discrimination. A specific setup is built and spectral measurements on several ex vivo porcine tissues are obtained. The massive data obtained are then analyzed for classification purposes. First of all, considerations about normalization, detrending and noise are taken into account. Dimensionality reduction and tendencies extraction are also considered. Featured spectral characteristics, principal component or linear discrimination analysis are applied, as long as classification approaches based on k-nearest neighbors (k-NN), quadratic discrimination analysis (QDA) or Naïve Bayes (NB). Relevant parameters about classification accuracy are obtained and compared, including ANOVA tests. The results show promising values of specificity and sensitivity of the technique for some classification algorithms, even over 95%, which could be relevant for clinical applications in the operating room.es_ES
dc.description.sponsorshipThis research was funded by the Ministry of Science, Innovation and Universities (Spain), grant number PGC2018-101464-B-I00, by the Ministry of Economy and Competitiveness (Spain), grant number RTC-2015-4285-1, and by San Cándido Foundationes_ES
dc.format.extent16 p.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceEntropy, 2020, 22(7), 736es_ES
dc.source37th Annual Congress of the Spanish Society of Biomedical Engineering (CASEIB), Santander, 2019es_ES
dc.subject.otherDiffuse reflectance spectroscopyes_ES
dc.subject.otherBiological tissueses_ES
dc.subject.otherTissue classificationes_ES
dc.subject.otherMultiple classificationes_ES
dc.titleApplication of classification algorithms to diuse reflectance spectroscopy measurements for ex vivo characterization of biological tissueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.3390/e22070736
dc.type.versionpublishedVersiones_ES


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.