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.

中文翻译：

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基于MKL-SVM级联特征和混合群体智能优化的肺CAD系统关键算法研究


特征选择和肺结节识别是肺部计算机辅助检测（Lung CAD）系统的核心模块。为了提高Lung CAD系统的性能，分别针对上述两部分进行了算法研究。首先，针对深层特征的可解释性较差以及临床定义的手工特征表达不完整的问题，提出了一种特征级联方法，以获得更丰富的结节特征信息作为分类器的最终输入。其次，为了更好地映射样本的全局特征，提出了多项式核和S形核的线性凸组合的多核学习支持向量机（MKL-SVM）算法。此外，本文应用速度收缩因子和轮盘策略的方法，并采用模拟退火（SA）和粒子群优化（PSO）混合进行全局优化，解决了PSO容易丢失的问题。粒子多样性并陷入局部最优解并提高模型的训练速度。因此，本文提出基于群体智能优化的MKL-SVM算法用于肺结节识别。最后，在合作医院数据集上进行了算法构建实验，并在公共数据集LUNA16上与8种先进算法进行了比较。实验结果表明，所提算法能够提高肺结节识别的准确率，减少结节的漏检。