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

Tao Hu | Zendy; Yuhui Lu | Zendy; Cungui Cheng | Zendy; Xiao-Chen Sun | Zendy

Open Access

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

Author(s) -

Tao Hu,

Yuhui Lu,

Cungui Cheng,

Xiao-Chen Sun

Publication year - 2011

Publication title -

spectroscopy an international journal

Language(s) - English

Resource type - Journals

eISSN - 1875-922X

pISSN - 0712-4813

DOI - 10.1155/2011/946783

Subject(s) - pattern recognition (psychology) , cancer , preprocessor , transformation (genetics) , artificial intelligence , feature extraction , discrete wavelet transform , wavelet , feature (linguistics) , fourier transform , wavelet transform , chemistry , computer science , mathematics , medicine , gene , biochemistry , mathematical analysis , linguistics , philosophy

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.

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