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Title:: A computer aided diagnosis model for multimodality lung tumor based on ensemble VPRS-RUGGA-Support Vector Machine

作者:: 张飞飞1 ; 周涛1; 2; 3△; 陆惠玲2 ; 梁蒙蒙1; 杨健1; 1.宁夏医科大学公共卫生与管理学院，银川 750000；2.宁夏医科大学理学院，银川 750000；3.宁夏智能信息与大数据处理重点实验室，银川 750021

Author(s):: ZHANG Feifei1; ZHOU Tao1; 2; 3; LU Huiling2; LIANG Mengmeng1; YANG Jian1; 1.School of Public Health and Management,Ningxia Medical University,Yinchuan 750000,China;2.School of Science,Ningxia Medical University,Yinchuan 750000；3.Ningxia Province Key Laboratory of Intelligent Information and Big Data Processing，Yinchuan 750021

Keywords:: Genetic algorithm; Variable precision rough set; Support vector machine; Computer aided diagnosis; Feature reduction

摘要:: 针对计算机辅助诊断模型优化过程中稳定性差和早熟问题，提出基于集成VPRS-RUGGA-支持向量机的肺部肿瘤计算机辅助诊断模型。首先，引入变精度粗糙集构造属性依赖度，结合属性约简长度和惩罚函数的加权和构造适应度函数框架；其次，采用无回放余数随机选择法、均匀交叉和高斯变异算子进行遗传操作；然后，在CT、PET和PET/CT样本空间中提取肺部肿瘤ROI区域特征，构造不同的特征空间，运用VPRS-RUGGA-支持向量机模型约简和分类识别；最后，在不同的样本空间中构造支持向量机（SVM）个体分类器，采用相对多数投票法输出集成结论。实验结果表明，集成VPRS-RUGGA-SVM模型可以有效的提高泛化性能和稳定性，VPRS-RUGGA-SVM模型可有效改善早熟问题，提高模型的分类性能。

Abstract:: Aiming at the problem of poor stability and premature in computer aided diagnosis model optimization procedure, to propose a computer aided diagnosis model of lung tumor based on ensemble VPRS- RUGGA-Support vector machine(SVM).Firstly,a fitness function framework was constructed based on weighted sum of three objective functions of variable precision rough set attribute dependency, attribute reduction length and penalty function;Secondly, remainder stochastic sampling with replacement,uniform crossover and Gaussian mutation operator were used to carry out genetic manipulation;Then,ROI region features were extracted of lung tumor in three sample spaces of CT, PET and PET/CT to construct different feature spaces, and using the VPRS-RUGGA-SVM model to reduce and classify.Finally, the SVM individual classifier was constructed in different sample spaces, and the relative majority voting method was used to output the ensemble conclusion. The experimental results show that the ensemble VPRS-RUGGA-SVM model can effectively improve the generalization performance and stability. VPRS-RUGGA-SVM model can relieve premature and improve the classification performance.