Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely\nused in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has\nachieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course\nof parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is\nintroduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm\nso as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertiaweights such\nas constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition.The\nexperimental results show that the model training time of the proposedMKL-SVM-PSO algorithm is only 1/7 of the training time\nof theMKL-SVMgrid search algorithm, achieving better recognition effect.Moreover, Euclidean norm of normalized error vector\nis proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence.Through\nstatistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial\nweight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the\nparameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence ismuch closer to the\noptimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.
Loading....