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.
