Li Yang, Zhu Zhichuan, Hou Alin, Zhao Qingdong, Liu Liwei, Zhang Lijuan
School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin 130024, China.
School of Computer Science and Engineering, Changchun University of Technology, Jilin 130012, China.
Comput Math Methods Med. 2018 Apr 29;2018:1461470. doi: 10.1155/2018/1461470. eCollection 2018.
Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.
肺结节识别是肺部计算机辅助检测（CAD）的核心模块。支持向量机（SVM）算法已广泛应用于肺结节识别，其中多核学习支持向量机（MKL - SVM）算法取得了良好效果。然而，基于网格搜索的MKL - SVM算法在参数优化过程中需要较长的优化时间，且其识别精度依赖于网格的精细程度。本文引入群体智能，将粒子群优化（PSO）与MKL - SVM算法相结合形成MKL - SVM - PSO算法，以快速实现参数的全局优化。为了获得全局最优解，将不同的惯性权重，如常数惯性权重、线性惯性权重和非线性惯性权重应用于肺结节识别。实验结果表明，所提MKL - SVM - PSO算法的模型训练时间仅为MKL - SVM网格搜索算法训练时间的1/7，取得了更好的识别效果。此外，提出了归一化误差向量的欧几里得范数来衡量收敛后平均适应度曲线与最优适应度曲线之间的接近程度。通过对不同惯性权重下20次运行结果的平均值进行统计分析可知，在MKL - SVM - PSO算法中动态惯性权重优于常数惯性权重。在动态惯性权重算法中，非线性惯性权重的参数优化时间更短；收敛后的平均适应度值更接近最优适应度值，优于线性惯性权重。此外，验证了一种更好的非线性惯性权重。
