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Conference Paper: Combined classifier for discriminating cancerous tissue from normal tissue using light-induced autofluorescence

TitleCombined classifier for discriminating cancerous tissue from normal tissue using light-induced autofluorescence
Authors
KeywordsAutofluorescence
Nasopharyngeal Carcinoma
Principal Component Analysis
Support Vector Machine
Issue Date2003
PublisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
The 2nd Diagnostic Optical Spectroscopy in Biomedicine Conference, Munich, Germany, 24 -25 June 2003. In Proceedings of SPIE - The International Society For Optical Engineering, 2003, v. 5141, p. 177-186 How to Cite?
AbstractWe investigated a novel method combining principal component analysis (PCA) and supervised learning technique, support vector machine (SVM), for classifying carcinoma lesion from normal tissue with light-induced autofluorescence. The autofluorescence spectral signals were collected in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. With the combined PCA and SVM classifying algorithm, the achieved overall accuracy is over 97%, companied with 95% sensitivity and 99% specificity for discriminating carcinoma from normal tissue. In comparison with the previously developed algorithms based on PCA method, this new method outperforms threshold- and probability-based PCA algorithms in all instances. The experimental results indicate great promise for autofluorescence spectroscopy based detection of small carcinoma lesion in the nasopharynx and other tissues.
Persistent Identifierhttp://hdl.handle.net/10722/172860
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorLin, WMen_US
dc.contributor.authorYuan, Xen_US
dc.contributor.authorYuen, PWen_US
dc.contributor.authorSham, Jen_US
dc.contributor.authorWei, WIen_US
dc.contributor.authorWen, Yen_US
dc.contributor.authorShi, PCen_US
dc.contributor.authorQu, JNen_US
dc.date.accessioned2012-10-30T06:25:22Z-
dc.date.available2012-10-30T06:25:22Z-
dc.date.issued2003en_US
dc.identifier.citationThe 2nd Diagnostic Optical Spectroscopy in Biomedicine Conference, Munich, Germany, 24 -25 June 2003. In Proceedings of SPIE - The International Society For Optical Engineering, 2003, v. 5141, p. 177-186en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/172860-
dc.description.abstractWe investigated a novel method combining principal component analysis (PCA) and supervised learning technique, support vector machine (SVM), for classifying carcinoma lesion from normal tissue with light-induced autofluorescence. The autofluorescence spectral signals were collected in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. With the combined PCA and SVM classifying algorithm, the achieved overall accuracy is over 97%, companied with 95% sensitivity and 99% specificity for discriminating carcinoma from normal tissue. In comparison with the previously developed algorithms based on PCA method, this new method outperforms threshold- and probability-based PCA algorithms in all instances. The experimental results indicate great promise for autofluorescence spectroscopy based detection of small carcinoma lesion in the nasopharynx and other tissues.en_US
dc.languageengen_US
dc.publisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_US
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.subjectAutofluorescenceen_US
dc.subjectNasopharyngeal Carcinomaen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectSupport Vector Machineen_US
dc.titleCombined classifier for discriminating cancerous tissue from normal tissue using light-induced autofluorescenceen_US
dc.typeConference_Paperen_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-s2.0-1342331802en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1342331802&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume5141en_US
dc.identifier.spage177en_US
dc.identifier.epage186en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLin, WM=8603475500en_US
dc.identifier.scopusauthoridYuan, X=36142338700en_US
dc.identifier.scopusauthoridYuen, PW=7103124007en_US
dc.identifier.scopusauthoridSham, J=24472255400en_US
dc.identifier.scopusauthoridWei, WI=7403321552en_US
dc.identifier.scopusauthoridWen, Y=55239414700en_US
dc.identifier.scopusauthoridShi, PC=7202161038en_US
dc.identifier.scopusauthoridQu, JN=36868346000en_US
dc.customcontrol.immutablesml 160321 - amend-

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