| 光谱学与光谱分析 |
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| Study of the Methods of Wavelet Feature Extraction and Neural Network Classification of Fluorescence Spectra to Improve the Diagnostic Rate of Colonic Earlier Stage Cancer |
| XIA Dai-lin1, MENG Hong-xia2, ZHANG Yang-de3, HE Ji-shan4 |
1. The Key Laboratory for Biomedical Photonics of Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China
2. Department of Automatization of Collage of Power & Mechanical Engineering of Wuhan University, Wuhan 430072, China
3. National Hepatobiliary and Enteric Surgery Research Center, Central South University, Changsha 410083, China
4. School of Info-physics and Geomatrics Engineering, Central South University, Changsha 410083，China |
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Abstract In order to improve the diagnostic rate of earlier stage colonic cancer with laser-induced 5-ALA-PpⅨ fluorescence spectra, a novel method of extraction of fluorescence spectral feature using wavelet analysis and classification using artificial neural network trained with resilient back-propagation algorithm (R-BPNN) was developed. 504 spectra were collected from 8 normal SD rats, and 20 1,2-DMH-induced SD colon cancer models and 12 second generation rats of induced rats. 150 min later trail intravenous injections of 5-ALA dose of 25 mg·kg-1 body weight (BW), and fluorescence spectra excited with 370 nm Ti-laser were collected in vivo. After preprocessing, 12 feature variants were extracted with wavelet analysis. With R-BPNN, all spectra were classified into two categories: normal or abnormal, which included dysplasia, early carcinoma (EC) and advanced carcinoma (AC). The sensitivity and specificity were 98.91% and 97.2% respectively. The accuracy of discriminating dysplasia, early carcinoma, and advanced carcinoma from normal tissue were 91.3%, 98.9% and 98.8% respectively. The result indicated that this method could effectively and easily diagnoses earlier stage colonic carcinomas.
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Received: 2005-08-10
Accepted: 2005-11-16
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Corresponding Authors:
XIA Dai-lin
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| Cite this article: |
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XIA Dai-lin,MENG Hong-xia,ZHANG Yang-de, et al. Study of the Methods of Wavelet Feature Extraction and Neural Network Classification of Fluorescence Spectra to Improve the Diagnostic Rate of Colonic Earlier Stage Cancer [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(11): 2076-2079.
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| URL: |
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https://www.gpxygpfx.com/EN/Y2006/V26/I11/2076 |
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