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Acta Chimica Sinica ›› 2007, Vol. 65 ›› Issue (22): 2539-2543. Previous Articles     Next Articles

应用小波特征提取肺癌组织FTIR的支持向量机分类方法研究

程存归*,1，田玉梅1，金文英2   

  1. (1浙江师范大学化学系 浙江省固体表面反应化学重点实验室 金华 321004)
    (2义乌工商职业技术学院计算机工程系 义乌 322000)
  • 投稿日期:2007-02-28 修回日期:2007-06-14 发布日期:2007-11-28
  • 通讯作者: 程存归

Study on the Methods of Wavelet Feature Extraction and SVM Classification of FTIR Lung Cancer Data

CHENG Cun-Gui*1; TIAN Yu-Mei1; JIN Wen-Ying2   

  1. (1 Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Department of Chemistry, Zhejiang Normal University, Jinhua 321004)
    (2 Department of Computer Science and Engineering, Yiwu Industrial and Commercial College, Yiwu 322000)
  • Received:2007-02-28 Revised:2007-06-14 Published:2007-11-28
  • Contact: CHENG Cun-Gui

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In order to improve the accuracy to earlier stage lung cancer diagnose rate with FTIR, a novel method of extraction of FTIR feature using wavelet analysis and classification using the support vector machine (SVM) was developed. To the FTIR of normal lung tissues, early carcinoma and advanced lung cancer, 9 feature variants were extracted with continuous wavelet (CW) analysis. With SVM, all spectra were classified into two categories: normal and abnormal ones, which included early lung cancer and advanced lung cancer. The accurate rates of poly and RBF kernel was high in all kernels. The accurate rates of poly kernel in normal, early lung cancer and advanced cancer were 100%, 95% and 100%, respectively and those of RBF kernel in normal, early lung cancer and advanced cancer were 100%, 95% and 100%, respectively. The research result shows the feasibility of establishing the models with an FTIR-CW-SVM method to identify normal lung tissue, early lung cancer and advanced lung cancer.

Key words: fourier transform infrared spectroscopy, wavelet feature extraction, support vector machine, early lung cancer