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Article: Classification of autofluorescence spectra using the algorithm based on support vector machine

TitleClassification of autofluorescence spectra using the algorithm based on support vector machine
Authors
Lin, WMYuan, XShi, PCQu, JNYuen, PWSham, JWei, WI
Issue Date2003
Citation
Osa Trends In Optics And Photonics Series, 2003, v. 88, p. 839-840 How to Cite?
AbstractA novel classification method based on support vector machine (SVM) technique is investigated to discriminate the cancerous tissue from normal tissue with light induced autofluorescence signals. The autofluorescence spectra were measured in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. It was found that the SVM based algorithms were able to achieve diagnostic accuracy with 94% sensitivity and over 96% specificity. In comparison with the previously developed algorithms based on principal component analysis, we found that the SVM algorithm produced better accuracy in diagnosing the cancerous tissue. ©2000 Optical Society of America.
Persistent Identifierhttp://hdl.handle.net/10722/173046
ISSN
1094-5695
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorLin, WMen_US
dc.contributor.authorYuan, Xen_US
dc.contributor.authorShi, PCen_US
dc.contributor.authorQu, JNen_US
dc.contributor.authorYuen, PWen_US
dc.contributor.authorSham, Jen_US
dc.contributor.authorWei, WIen_US
dc.date.accessioned2012-10-30T06:26:57Z-
dc.date.available2012-10-30T06:26:57Z-
dc.date.issued2003en_US
dc.identifier.citationOsa Trends In Optics And Photonics Series, 2003, v. 88, p. 839-840en_US
dc.identifier.issn1094-5695en_US
dc.identifier.urihttp://hdl.handle.net/10722/173046-
dc.description.abstractA novel classification method based on support vector machine (SVM) technique is investigated to discriminate the cancerous tissue from normal tissue with light induced autofluorescence signals. The autofluorescence spectra were measured in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. It was found that the SVM based algorithms were able to achieve diagnostic accuracy with 94% sensitivity and over 96% specificity. In comparison with the previously developed algorithms based on principal component analysis, we found that the SVM algorithm produced better accuracy in diagnosing the cancerous tissue. ©2000 Optical Society of America.en_US
dc.languageengen_US
dc.relation.ispartofOSA Trends in Optics and Photonics Seriesen_US
dc.titleClassification of autofluorescence spectra using the algorithm based on support vector machineen_US
dc.typeArticleen_US
dc.identifier.emailWei, WI: hrmswwi@hku.hken_US
dc.identifier.authorityWei, WI=rp00323en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-8744226621en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-8744226621&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume88en_US
dc.identifier.spage839en_US
dc.identifier.epage840en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLin, WM=8603475500en_US
dc.identifier.scopusauthoridYuan, X=36142338700en_US
dc.identifier.scopusauthoridShi, PC=7202161038en_US
dc.identifier.scopusauthoridQu, JN=7201534954en_US
dc.identifier.scopusauthoridYuen, PW=7103124007en_US
dc.identifier.scopusauthoridSham, J=24472255400en_US
dc.identifier.scopusauthoridWei, WI=7403321552en_US
dc.identifier.issnl1094-5695-

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