基于支持向量机的^(31)P磁共振波谱肝细胞癌诊断

^(31)P MRS data diagnosis of hepatocellular carcinoma based on support vector machine

支持向量机是在统计学习理论基础上发展起来的一种新的机器学习方法,在模式识别领域有着广泛的应用。利用基于支持向量机模型的31P磁共振波谱数据对肝脏进行分类,区别肝细胞癌,肝硬化和正常的肝组织。通过对基于多项式核函数和径向基核函数的支持向量机分类器进行比较,并且得到三种肝脏分类的识别率。实验表明基于31P磁共振波谱数据的支持向量机分类模型能够对活体肝脏进行诊断性的预测。

SVM（Support Vector Machine） is a new machine-learning technique which is developed based on statistical theory and has many applications in pattern recognition.We use SVM model based on 31P MRS to distinguish three diagnostic types of hepatocellular carcinoma,hepatic cirrhosis and normal hepatic tissue.The classification accuracy of SVM based on polynomial and radial basis function kernel were compared,and the recognition accuracy of the three categories were obtained.The result of experiments shows that SVM model based on 31P MRS provides diagnostic prediction of liver in vivo.