Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Abstract

Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

System Construction of Mind Map in English Vocabulary Teaching Based on Machine Learning Algorithm

“…Chang et al proposed a pulmonary nodule identification algorithm based on machine learning, finally carried out the algorithm construction experiment on the cooperative hospital dataset, and compared it with 8 advanced algorithms on the public dataset LUNA16. e final experimental results show that the proposed algorithm can improve the accuracy of pulmonary nodule recognition and can reduce the missed detection of nodules [3]. Sarkar et al used an optimized machine learning algorithm to predict the outcome of accidents such as injuries, near misses, and property damage using occupational accident data and finally validated the effectiveness of the proposed method through experiments [4].…”

Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study

“…Our results indicated that compared with the tandem strategy in other radiomic models, the MKL-SVM algorithm with a linear convex combination of polynomial kernel and sigmoid kernel could effectively fuse bimodal data with better performance in differential diagnosis. The MKL has been demonstrated to improve classification accuracy and robustness [35] . It is impossible to construct a universal optimal learning algorithm in all fields.…”

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

“…After image preprocessing consistent with the literature 55 , 1140 ROI images of candidate nodules with a size of 64*64 were selected as the experimental dataset. Specifically, 650 images of solitary lung nodules and 490 non-nodule images were included.…”