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。
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%.
出处
《生物信息学》
2010年第2期171-174,共4页
Chinese Journal of Bioinformatics
基金
山东省自然科学基金(Y2006C96)
山东省自然科学基金(Y2008G30)
SRF for ROCS
SEM
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
31 Phosphorus
Magnetic Resonance Spectroscopy
hepatocellular carcinoma
Back - propagation neural network （BP）
Radial Basis Function Neural Network（RBF）
