Identification of Cancer in CT Images based on SVM and PSO using Gene Selection Algorithm

Bhyratae, Suhas A; Mahesh, Shanthi; Harshith, A. S.; Shekar, Harshitha C.

doi:10.17485/ijst/2016/v9i48/106499

Cited by 1 publication

(1 citation statement)

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“…e swarm intelligence optimization strategy has the characteristics of fast solving speed, high accuracy, wide application range, and strong stability, and it is widely used in the parameter optimization of machine-learning algorithms [39,40]. Typical swarm intelligence optimization includes PSO [41], Ant Colony Optimization (ACO) [42], Artificial Bee Colony (ABC) [43], etc. In recent years, new swarm intelligence optimization algorithms such as Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Grasshopper Optimization Algorithm (GOA) have also emerged and excelled in solving a variety of optimization problems such as quadratic planning and convex planning [44][45][46][47][48][49].…”

Section: Parameter Optimization Algorithmmentioning

confidence: 99%

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

Chang

Zheng

2021

Computational Intelligence and Neuroscience

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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.

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