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Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification
Li, Qingbo ; Wang, Wei ; Ling, Xiaofeng ; Wu, Jin Guang
刊名biomed research international
2013
关键词ENDOSCOPIC BIOPSIES CELLS DIAGNOSIS SPECTRA TISSUES
DOI10.1155/2013/942427
英文摘要Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000323444200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Biotechnology & Applied Microbiology; Medicine, Research & Experimental; SCI(E); 3; ARTICLE
语种英语
内容类型期刊论文
源URL[http://ir.pku.edu.cn/handle/20.500.11897/392265]  
专题化学与分子工程学院
推荐引用方式
GB/T 7714
Li, Qingbo,Wang, Wei,Ling, Xiaofeng,et al. Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification[J]. biomed research international,2013.
APA Li, Qingbo,Wang, Wei,Ling, Xiaofeng,&Wu, Jin Guang.(2013).Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification.biomed research international.
MLA Li, Qingbo,et al."Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification".biomed research international (2013).
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