MKL-SVM algorithm for pulmonary nodule recognition based on swarm intelligence optimization

To solve the problem that a single kernel learning support vector machine (SVM) cannot consider the learning and generalization abilities and parameter optimization of the multiple kernel function, a multiple kernel learning support vector machine (MKL-SVM) algorithm based on swarm intelligence optimization was proposed. First, the impact of five single kernel functions on the classification indexes of SVM was discussed. These kernel functions include two global kernel functions — the polynomial and sigmoid kernel functions — and three local kernel functions—the radial basis function, exponential kernel function, and Laplacian kernel function. Next, an MKL-SVM algorithm with a convex combination of a polynomial kernel having global properties and a Laplacian kernel having local properties was proposed. Then, to improve particle diversity to avoid falling into local optimal solutions during the iteration, and to reduce the model’s training time, the crossover operation in the genetic algorithm was introduced into the particle swarm optimization (PSO) algorithm. This improved swarm intelligence optimization was used to optimize the parameters of the MKL-SVM. Finally, deep learning features based on the classical model VGG16 and handcrafted features according to doctors’ suggestions were used as inputs for the recognition algorithm. In this algorithm, transfer learning was used to extract deep learning features and principal component analysis was used to reduce computational complexity through dimensionality reduction. The results show that using deep learning features is better than handcrafted features. Therefore, this paper adopts the deep learning features as input for the MKL-SVM algorithm and the hybrid swarm intelligent optimization algorithm of crossover genetic and the PSO algorithm as the optimization method. To verify the generalization ability of the proposed algorithm, the public dataset LUNA16 was selected for testing. The experimental results show that the proposed algorithm is easy to jump out of the local optimal solution, improves the learning ability and generalization ability of the algorithm, and has a better classification performance.