CAJ | 학술논문

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~(31)P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~(31)P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.
지지향량궤시재통계학습이론기출상발전기래적일충신적궤기학습방법,재모식식별영역유착엄범적응용.이용기우지지향량궤모형적~(31)P자공진파보수거대간장진행분류,구별간세포암,간경화화정상적간조직.통과대기우다항식핵함수화경향기핵함수적지지향량궤분류기진행비교,병차득도삼충간장분류적식별솔.실험표명기우~(31)P자공진파보수거적지지향량궤분류모형능구대활체간장진행진단성적예측.
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.