| 神经网络提高肝细胞癌磁共振波谱诊断正确率 |
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| 引用本文： | 王丽娟,刘毅慧,刘强,李保朋,成金勇. 神经网络提高肝细胞癌磁共振波谱诊断正确率[J]. 生物信息学, 2010, 8(2): 171-174 |
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| 作者姓名： | 王丽娟 刘毅慧 刘强 李保朋 成金勇 |
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| 作者单位： | 1. 山东轻工业学院,信息科学与技术学院,智能信息处理研究所,济南,250353
2. 山东省医学影像学研究所,济南,250021 |
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| 基金项目： | 山东省自然科学基金，山东省自然科学基金，SRF for ROCS |
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| 摘 要： | 通过评价31磷磁共振波谱(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)来辨别三种诊断类型:肝细胞癌,正常肝和肝硬化。运用反向传输神经网络(BP)和径向基函数神经网络(RBF)分析31P-MRS数据,分别建立神经网络模型,进行肝细胞癌的诊断分类以期提高识别率。实验结果证明,应用神经网络模型后,31P-MR波谱对活体肝细胞癌的诊断正确率从89.47%提高到97.3%,且BP更优于RBF。
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| 关 键 词： | 31磷 磁共振波谱 肝细胞癌 反向传输神经网络 径向基函数神经网络 |
Study using neural networks improve the diagnostic accuracy rate of magnetic resonance spectroscopy in hepatocelluar carcinoma |
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| WANG Li-juan,LIU Yi-hui,LIU Qiang,LI Bao-peng,CHENG Jin-yong. Study using neural networks improve the diagnostic accuracy rate of magnetic resonance spectroscopy in hepatocelluar carcinoma[J]. Chinese Journal of Bioinformatics, 2010, 8(2): 171-174 |
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| Authors: | WANG Li-juan LIU Yi-hui LIU Qiang LI Bao-peng CHENG Jin-yong |
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| Affiliation: | 1. Institute of Intelligence Information Processing, School of Information Science and Technology, Shandong Institute of Light Industry,Jinan 250353 ,China;2. Shandong Medical Imaging Research lnstitute,Jinan 250021, China) |
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| Abstract: | Through the evaluation of the 31 Phosphorus Magnetic Resonance Spectroscopy（ 31p- MRS） ,we can distinguish three types of diagnosis： hepatocellular carcinoma, normal and cirrhosis. 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 to improve the recognition rate. The results suggest that BP models have better performance than RBF models. After application of neural network models, the diagnostic accuracy rate of hepatocellular carcinoma is improved from 89.47% to 97. 3%. |
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| Keywords: | 31 Phosphorus Magnetic Resonance Spectroscopy hepatocellular carcinoma Back - propagation neural network （BP） Radial Basis Function Neural Network（RBF） |
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