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Spectroscopy
Volume 26, Issue 3, Pages 155-165
http://dx.doi.org/10.3233/SPE-2011-0535

Study on the early detection of gastric cancer based on discrete wavelet transformation feature extraction of FT-IR spectra combined with probability neural network

Tao Hu,1,2,6 Yu-Hui Lu,3 Cun-Gui Cheng,4 and Xiao-Chen Sun5

1Faculty of Life Science and Chemical Engineering, Huaiyin Institute of Technology, Huaian, China
2National Special Superfine Powder Engineering Center, Nanjing University of Science and Technology, Nanjing, China
3The First College of Clinic Medicine, Zhejiang Chinese Medical University, Hangzhou, China
4Department of Chemistry, Zhejiang Normal University, Jinhua, China
5Department of Physics, Zhejiang Normal University, Jinhua, China
6Faculty of Life Science and Chemical Engineering, Huaiyin Institute of Technology, Huaian 223003, China

Copyright © 2011 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.

Abstract

This paper introduces a new method for the early detection of gastric cancer using a combination of feature extraction based on discrete wavelet transformation (DWT) for horizontal attenuated total reflectance–Fourier transform infrared spectroscopy (HATR–FT-IR) and classification using probability neural network (PNN). 344 FT-IR spectra were collected from 172 pairs of fresh normal and abnormal stomach tissue᾽s samples. After preprocessing, 5 features were extracted with DWT analysis. Based on the PNN classification, all FT-IR spectra were classified into three categories. The accuracy of identifying normal gastric tissue, early gastric cancer tissue and gastric cancer tissue samples were 100.00, 97.56 and 100.00%, respectively. This result indicated that FT-IR with DWT and PNN could effectively and easily diagnose gastric cancer in its early stages.