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31Phosphorus Magnetic Resonance Spectroscopy Data Analysis of the Hepatocellular Carcinoma Using Artificial Neural Networks

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5 Author(s)
Lijuan Wang ; Sch. of Inf. Sci. & Technol., Inst. of Intell. Inf. Process., Jinan, China ; Yihui Liu ; Qiang Liu ; Jinyong Cheng
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Through the evaluation of the 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS), we can distinguish three types of diagnosis: hepatocellular carcinoma, normal and cirrhosis. 71 samples of 31P-MRS data are selected including hepatocellular carcinoma, normal and cirrhosis tissue. Back-propagation neural network (BP) and Radial Basis Function Neural Network (RBF) are applied to analyze 31P-MRS data, develop neural network models of 31P-MRS for the diagnostic classification of hepatocellular carcinoma. The results suggest that BP models have better performance than RBF models. Neural network models based on 31P-MRS data offer an alternative and promising technique for diagnostic prediction of hepatocellular carcinoma in vivo.

Published in:

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on  (Volume:3 )

Date of Conference:

21-22 Nov. 2009