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Article: Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

TitleLaser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities
Authors
Issue Date2013
Citation
Laser Physics, 2013, v. 23, n. 3, article no. 035603 How to Cite?
AbstractThe existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. © 2013 Astro Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/296080
ISSN
2023 Impact Factor: 1.2
2023 SCImago Journal Rankings: 0.291
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDu, Zhanwei-
dc.contributor.authorYang, Yongjian-
dc.contributor.authorBai, Yuan-
dc.contributor.authorWang, Lijun-
dc.contributor.authorSu, Le-
dc.contributor.authorChen, Yong-
dc.contributor.authorLi, Xianchang-
dc.contributor.authorZhou, Xiaodong-
dc.contributor.authorJia, Jun-
dc.contributor.authorShen, Aiguo-
dc.contributor.authorHu, Jiming-
dc.date.accessioned2021-02-11T04:52:47Z-
dc.date.available2021-02-11T04:52:47Z-
dc.date.issued2013-
dc.identifier.citationLaser Physics, 2013, v. 23, n. 3, article no. 035603-
dc.identifier.issn1054-660X-
dc.identifier.urihttp://hdl.handle.net/10722/296080-
dc.description.abstractThe existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. © 2013 Astro Ltd.-
dc.languageeng-
dc.relation.ispartofLaser Physics-
dc.titleLaser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1088/1054-660X/23/3/035603-
dc.identifier.scopuseid_2-s2.0-84879101664-
dc.identifier.volume23-
dc.identifier.issue3-
dc.identifier.spagearticle no. 035603-
dc.identifier.epagearticle no. 035603-
dc.identifier.eissn1555-6611-
dc.identifier.isiWOS:000318005700024-
dc.identifier.issnl1054-660X-

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