Journal of Spectroscopy

Journal of Spectroscopy / 2008 / Article

Open Access

Volume 22 |Article ID 182564 | https://doi.org/10.3233/SPE-2008-0352

Cun-Gui Cheng, Yu-Mei Tian, Wen-Ying Jin, "A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR", Journal of Spectroscopy, vol. 22, Article ID 182564, 8 pages, 2008. https://doi.org/10.3233/SPE-2008-0352

A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR

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.

Copyright © 2008 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Order printed copiesOrder
Views350
Downloads657
Citations

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.