Affiliations: Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Department of Chemistry, Zhejiang Normal University, Jinhua 321004, P. R. China | Department of Computer Science and Engineering, Yiwu Industrial and Commercial College, Yiwu 322000, P. R. China
Note: [] Corresponding author: Cun-Gui Cheng, Department of Chemistry, Zhejiang Normal University, Zhejiang, Jinhua 321004, P. R. China. Tel.: +86 0579 83126962; Fax: +86 0579 82282489; E-mail: [email protected].
Abstract: This paper introduces a new method for the early detection of colon cancer using a combination of feature extraction based on wavelets for Fourier Transform Infrared Spectroscopy (FTIR) and classification using the Support Vector Machine (SVM). The FTIR data collected from 36 normal SD rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats was first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. The extracted feature variants were then inputted into the SVM for classification of normal, dysplasia, early carcinoma, and advanced carcinoma. Among the kernel functions the SVM used, the Poly and RBF kernels had the highest accuracy rates. The accuracy of the Poly kernel in normal, dysplasia, early carcinoma, and advanced carcinoma were 100, 97.5, 95% and 100% respectively. The accuracy of RBF kernel in normal, dysplasia, early carcinoma, and advanced carcinoma was 100, 95, 95% and 100% respectively. The results indicated that this method could effectively and easily diagnose colon cancer in its early stages.
Keywords: FTIR, wavelet feature extraction, SVM, colonic earlier stage cancer
DOI: 10.3233/SPE-2008-0352
Journal: Spectroscopy, vol. 22, no. 5, pp. 397-404, 2008
