[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].
In order to improve the diagnostic rate of earlier stage colonic cancer with laser-induced 5-ALA-Pp IX 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 x 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.
Xia
Dai-lin
DL
The Key Laboratory for Biomedical Photonics of Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China.
Meng
Hong-xia
HX
Zhang
Yang-de
YD
He
Ji-shan
JS
D000818:Animals; D003110:Colonic Neoplasms / Q000175:diagnosis*; D004195:Disease Models, Animal; D016571:Neural Networks, Computer; D051381:Rats; D013050:Spectrometry, Fluorescence / Q000379:methods*