基于模糊贝叶斯网络的星形细胞瘤分级预测与知识表达

针对医学影像特征具有模糊性和不确定性的特点，提出一种基于模糊贝叶斯网络的影像诊断预测模型。该模型使用高斯混合模型（GMM）对连续的视觉特征进行模糊量化处理，利用专家知识根据病症与影像特征之间的因果关系建立混合贝叶斯网络结构；由数据通过机器学习确定网络参数；采用概率推理定量估计病症的发生概率，从而建立一个可计算的预测模型。将该方法应用于星形细胞瘤分级预测，实验结果得出83．33％的正确识别率，远远超过使用最小近邻分类器（K-NN）实现连续变量硬（crisp）量化的贝叶斯网络模型，更合理地表达了具有模糊性、不确定性的专业领域的结构性知识，为星形细胞瘤恶性程度预测提供了新的辅助手段。

Prediction and Knowledge Expression of Astrocytoma Malignant Degree Based on Fuzzy Bayesian Networks

A modified fuzzy Bayesian network(FBN) is proposed in this study.It uses Gaussian mixture models(GMM) to make a fuzzy procedure for continuous image features.This particular procedure will transform continuous variables into discrete ones by soft quantizers,when dealing with continuous inputs with probabilistic and uncertain nature.It builds a hybrid Bayesian network(BN) construction modeling the causality of image features and diseases with expert knowledge,and trains the BN with data through machine learning,and estimates a probability of diseases by probability inference.This method is applied in prediction of the astrocytoma malignant degree and achieves an accuracy of 83.33%,which outperforms the BN using a crisp quantizer by a k-nearest neighbor classifier.This model provides more reasonable knowledge expression for domains with fuzzy and uncertain nature and a novel objective intelligent method to quantitatively assess the astrocytoma malignant level that can be used to assist doctors to diagnose the tumor.