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Article: Classification of in VIVO autofluorescence spectra using support vector machines

TitleClassification of in VIVO autofluorescence spectra using support vector machines
Authors
KeywordsFluorescence spectroscopy
Support vector machines
Tissue diagnosis
Issue Date2004
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/jbo
Citation
Journal Of Biomedical Optics, 2004, v. 9 n. 1, p. 180-186 How to Cite?
AbstractAn aglorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. Leave-one-out cross-validation was used to evaluate the performance of the algorithms. An overall diagnostic accuracy of 96%, a sensitivity of 94%, and a specificity of 97% for discriminating nasopharyngeal carcinomas from normal tissues were achieved using a linear SVM algorithm. A diagnostic accuracy of 98%, a sensitivity of 95%, and a specificity of 99% for detecting NPC were achieved with a nonlinear SVM algorithm. In a comparison with previously developed algorithms using the same dataset and the principal component analysis (PCA) technique, the SVM algorithms produced better diagnostic accuracy in all instances. In addition, we investigated a method combining PCA and SVM techniques for reducing the complexity of the SVM algorithms. © 2004 Society of Photo-Optical Instrumentation Engineers.
Persistent Identifierhttp://hdl.handle.net/10722/42651
ISSN
2015 Impact Factor: 2.556
2015 SCImago Journal Rankings: 1.201
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLin, Wen_HK
dc.contributor.authorYuan, Xen_HK
dc.contributor.authorYuen, Pen_HK
dc.contributor.authorWei, WIen_HK
dc.contributor.authorSham, Jen_HK
dc.contributor.authorShi, Pen_HK
dc.contributor.authorQu, Jen_HK
dc.date.accessioned2007-03-23T04:28:54Z-
dc.date.available2007-03-23T04:28:54Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal Of Biomedical Optics, 2004, v. 9 n. 1, p. 180-186en_HK
dc.identifier.issn1083-3668en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42651-
dc.description.abstractAn aglorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. Leave-one-out cross-validation was used to evaluate the performance of the algorithms. An overall diagnostic accuracy of 96%, a sensitivity of 94%, and a specificity of 97% for discriminating nasopharyngeal carcinomas from normal tissues were achieved using a linear SVM algorithm. A diagnostic accuracy of 98%, a sensitivity of 95%, and a specificity of 99% for detecting NPC were achieved with a nonlinear SVM algorithm. In a comparison with previously developed algorithms using the same dataset and the principal component analysis (PCA) technique, the SVM algorithms produced better diagnostic accuracy in all instances. In addition, we investigated a method combining PCA and SVM techniques for reducing the complexity of the SVM algorithms. © 2004 Society of Photo-Optical Instrumentation Engineers.en_HK
dc.format.extent147838 bytes-
dc.format.extent2116 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/jboen_HK
dc.relation.ispartofJournal of Biomedical Opticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsCopyright 2004 Society of Photo-Optical Instrumentation Engineers. This paper was published in Journal of Biomedical Optics, 2004, v. 9 n. 1, p. 180-186 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en_HK
dc.subjectFluorescence spectroscopyen_HK
dc.subjectSupport vector machinesen_HK
dc.subjectTissue diagnosisen_HK
dc.titleClassification of in VIVO autofluorescence spectra using support vector machinesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1083-3668&volume=9&issue=1&spage=180&epage=186&date=2004&atitle=Classification+of+in+vivo+autofluorescence+spectra+using+support+vector+machinesen_HK
dc.identifier.emailWei, WI: hrmswwi@hku.hken_HK
dc.identifier.authorityWei, WI=rp00323en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1117/1.1628244en_HK
dc.identifier.scopuseid_2-s2.0-1642602735en_HK
dc.identifier.hkuros85927-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1642602735&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume9en_HK
dc.identifier.issue1en_HK
dc.identifier.spage180en_HK
dc.identifier.epage186en_HK
dc.identifier.isiWOS:000188247700019-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLin, W=8603475500en_HK
dc.identifier.scopusauthoridYuan, X=36142338700en_HK
dc.identifier.scopusauthoridYuen, P=7103124007en_HK
dc.identifier.scopusauthoridWei, WI=7403321552en_HK
dc.identifier.scopusauthoridSham, J=24472255400en_HK
dc.identifier.scopusauthoridShi, P=7202161038en_HK
dc.identifier.scopusauthoridQu, J=7201534954en_HK

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