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作者

Qiang Liu Ning Wang Yi Hui Liu Shao Qing Wang Jin Yong Cheng Dong Yue Yu Jie Chen

作者单位

Taishan Med Coll, Tai An, Shandong, Peoples R China Shandong Inst Light Ind, Sch Comp Sci, Jinan, Peoples R China Shandong Med Imaging Res Inst, Jinan 250021, Shandong, Peoples R China

刊名

INFORMATION TECHNOLOGY FOR MANUFACTURING SYSTEMS, PTS 1 AND 2

年份

2010

页码

630

ISSN

1662-7482

关键词

³¹Phosphorus Magnetic Resonance Spectroscopy Back-Propagation Neural Network (BPNN) Diagnostic Prediction Hepatic Diseases Non Invasive Technique Self-Oorganizing Map (SOM) Neural Network Spectrum Curve

摘要

31P MRS 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 and self-organizing map 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 exp...更多

31P MRS 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 and self-organizing map 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.收起

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