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基于群体智能优化的MKL-SVM算法及肺结节识别

李阳 常佳乐 王宇阳

李阳, 常佳乐, 王宇阳. 基于群体智能优化的MKL-SVM算法及肺结节识别[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.14.004
引用本文: 李阳, 常佳乐, 王宇阳. 基于群体智能优化的MKL-SVM算法及肺结节识别[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.14.004
LI Yang, CHANG Jia-yue, WANG Yu-yang. MKL-SVM algorithm for pulmonary nodule recognition based on swarm intelligence optimization[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.14.004
Citation: LI Yang, CHANG Jia-yue, WANG Yu-yang. MKL-SVM algorithm for pulmonary nodule recognition based on swarm intelligence optimization[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.14.004

基于群体智能优化的MKL-SVM算法及肺结节识别

doi: 10.13374/j.issn2095-9389.2021.01.14.004
基金项目: 国家自然科学基金资助项目( 61806024); 吉林省教育厅十三五科研规划项目(JJKH20181041KJ, JJKH20200680KJ); 吉林省科技发展计划项目(20200401103GX)
详细信息
    通讯作者:

    E-mail:liyangyaya1979@sina.com

  • 中图分类号: TP391.4

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

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  • 摘要: 针对单核学习支持向量机无法兼顾学习能力与泛化能力以及多核函数参数寻优问题,提出了一种基于群体智能优化的多核学习支持向量机算法。首先,研究了五种单核函数对支持向量机分类性能的影响,进一步提出具有全局性质的多项式核和局部性质的拉普拉斯核凸组合形式的多核学习支持向量机算法;其次,为增加粒子多样性及快速寻优,将粒子群优化算法引入了遗传算法中的杂交操作,并用此改进的群体智能优化算法对多核学习支持向量机进行参数寻优。最后,分别采用深度特征与手工特征作为识别算法的输入,研究表明采用深度特征优于手工特征。故本文采用深度特征作为多核学习支持向量机的输入,以交叉遗传与粒子群混合智能优化算法作为其寻优方式。实验选取合作医院数据集对所提算法进行训练并初步测试,进一步为了验证所提算法的泛化能力,选取公开数据集LUNA16进行测试。实验结果表明,本文算法易于跳出局部最优解,提升了算法的学习能力与泛化能力,具有较优的分类性能。

     

  • 图  1  不同核函数的全局性与局部性分析。(a)多项式核;(b)感知机核;(c)高斯核;(d)指数核;(e)拉普拉斯核

    Figure  1.  Global and local analyses of various kernel functions: (a) polynomial kernel; (b) sigmoid kernel; (c) Gaussian kernel; (d) exponential kernel; (e) Laplacian kernel

    图  2  GAPSO的算法流程图

    Figure  2.  Flowchart of the GAPSO algorithm

    图  3  不同算法的ROC曲线图及PR曲线图。(a)不同核函数SVM算法的ROC曲线;(b)不同核函数SVM算法的PR曲线;(c)不同寻优方式MKL-SVM算法的ROC曲线;(d)不同寻优方式MKL-SVM算法的PR曲线

    Figure  3.  ROC and PR curves of various algorithms: (a) ROC curves of SVM algorithms with various kernel functions; (b) PR curves of SVM algorithms with various kernel functions; (c) ROC curves of the MKL-SVM algorithm with various optimization algorithms; (d) PR curves of the MKL-SVM algorithm with various optimization algorithms

    图  4  本文算法的适应度曲线

    Figure  4.  Fitness curve of the proposed algorithm

    图  5  深度特征结合本文算法的适应度曲线

    Figure  5.  Fitness curve combining deep learning features with the proposed algorithm

    表  1  不同核函数的实验结果

    Table  1.   Experimental results of various kernel functions

    AlgorithmACC_mean/%ACC_max/%MASEN/%MASPE/%F1_score/%MCC/%AUCAP
    Polynomial kernel + GAPSO90.0090.0085.1991.7882.1475.300.95840.8506
    Sigmoid kernel + GAPSO89.0089.0077.7893.1581.2674.350.94820.7990
    RBF kernel + GAPSO90.5091.0088.8991.7882.8576.390.94980.8022
    Exponential kernel + GAPSO90.4091.0092.5990.4183.7177.600.96040.8470
    Laplacian kernel + GAPSO90.6091.0092.5990.4184.1878.230.96550.8464
    MKL-SVM + PSO90.8092.0088.8993.1583.6877.440.96090.8726
    MKL-SVM + GA89.5090.0092.5989.0482.5075.930.96190.8830
    MKL-SVM + GAPSO91.1092.0088.8993.1584.3078.290.96500.8984
    下载: 导出CSV

    表  2  深度特征结合本文算法的实验结果

    Table  2.   Results of the proposed algorithm combined with deep learning features

    AlgorithmACC_mean/%ACC_max/%MASEN/%MASPE/%F1_score/%MCC/%AUCAP
    Handcrafted features + MKL-SVM + GAPSO91.1092.0088.8993.1584.3078.290.96500.8984
    Deep learning features + MKL-SVM + GA88.0088.0081.8291.0481.8272.860.90380.8755
    Deep learning features + MKL-SVM + PSO89.8091.0075.7697.0182.5776.810.94840.9038
    Deep learning features + MKL-SVM + GAPSO
    (Proposed work)
    91.5094.0081.8210085.8180.690.95880.9043
    下载: 导出CSV

    表  3  所提算法与当前主流算法的性能比较

    Table  3.   Performance comparison of the proposed algorithm with current state-of-the-art methods

    ReferencesYearDatasetsMethodsACC/%SEN/%SPE/%AUC
    Zhao et al. [24]2019LIDC-IDRI (743 images)Transfer learning CNNs85.0094.000.94
    Masood et al. [25]2020LIDC-IDRI (892 images)Enhanced multidimensional region-based fully CNN97.9198.193.20.9813
    Mastouri et al. [14]2020LUNA16 (3186 images)Bilinear CNN + SVM91.9991.8592.270.959
    Proposed work2021LUNA16 (1140 images)Deep learning features+ Improved MKL-SVM95.2994.8595.890.9803
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-01-14
  • 网络出版日期:  2021-08-26

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