Paper Title:
31P MRS Data Analysis of Liver Cancer Based on Neural Networks
  Abstract

31P MRS(31Phosphorus Magnetic Resonance Spectroscopy) is a non invasive protocol for analyzing the energetic metabolism and biomedical changes in cellular level. Evaluation of 31P MRS is important in diagnosis and treatment of many hepatic diseases. In this paper, we apply back-propagation neural network (BP) and self-organizing map (SOM) neural network to analyze 31P MRS data to distinguish three diagnostic classes of cancer, normal and cirrhosis tissue. 66 samples of 31P MRS data are selected including cancer, normal and cirrhosis tissue. Four experiments are carried out. Good performance is achieved with limited samples. Experimental results prove that neural network models based on 31P MRS data offer an alternative and promising technique for diagnostic prediction of liver cancer in vivo.

  Info
Periodical
Edited by
Qi Luo
Pages
630-635
DOI
10.4028/www.scientific.net/AMM.20-23.630
Citation
Q. Liu, N. Wang, Y. H. Liu, S. Q. Wang, J. Y. Cheng, D. Y. Yu, J. Chen, "31P MRS Data Analysis of Liver Cancer Based on Neural Networks", Applied Mechanics and Materials, Vols. 20-23, pp. 630-635, 2010
Online since
January 2010
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