基于PSO-ConvK卷积神经网络的肺部肿瘤图像识别

doi:10.6040/j.issn.1672-3961.0.2018.191

摘要/Abstract

摘要：

针对卷积核随机初始化以及梯度下降法训练卷积神经网络易陷入局部最值问题,提出粒子群算法优化卷积核(particle swarm optimization-convolution kernel, PSO-ConvK)的图像识别方法。使用参数迁移法构造卷积神经网络,并提取卷积核,利用PSO不断更新粒子的速度和位置,寻找全局最优值以初始化卷积核,将其传递到卷积神经网络,用肺部肿瘤数据训练卷积神经网络,结合梯度下降法修正网络权重,使得PSO算法的全局优化能力与梯度下降法的局部搜索能力相结合。试验通过批次大小、迭代次数以及网络层数3个角度验证方法的有效性,并与高斯函数优化卷积核进行对比。结果显示, PSO优化卷积核的识别率始终高于随机化卷积核和高斯卷积核,识别率最终达到98.3%,具有一定的可行性和优越性。

关键词: 粒子群算法, 卷积核, 卷积神经网络, 肺部肿瘤, 医学图像

Abstract:

In order to solve problems that convolution kernel was random initialization and the gradient descent method to train convolution neural network was easy to fall into local minimum, an image recognition method based on particle swarm optimization for convolution kernel was proposed. CNN(convolution neural network) was constructed by using the parameter migration method, and convolution kernel was extracted. The particle swarm algorithm was used to update the velocity and position of particles constantly and find the global optimal value to initialize convolution kernels. Convolution kernels were transferred to convolution neural network, and lung tumor images were used to train them. CNN model was trained by lung tumor images, and gradient descent method was used to modify network weights, hence global optimization ability of PSO algorithm was combined with local search ability of gradient descent method. The experiments verified effectiveness of method through three perspectives: batch sizes, iteration numbers, and network layer numbers. The particle swarm algorithm was compared with gauss function. The recognition rates of PSO optimized convolution kernel were always higher than that of randomized convolution kernel and gauss convolution kernel, its recognition rate reached 98.3%, which had certain feasibility and superiority.

Key words: PSO, convolution kernel, convolutional neural network, lung tumor, medical images

中图分类号:

TP183

梁蒙蒙,周涛,夏勇,张飞飞,杨健. 基于PSO-ConvK卷积神经网络的肺部肿瘤图像识别[J]. 山东大学学报 (工学版), 2018, 48(5): 77-84.

Mengmeng LIANG,Tao ZHOU,Yong XIA,Feifei ZHANG,Jian YANG. Lung tumor images recognition based on PSO-ConvK convolutional neural network[J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 77-84.

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迭代次数	随机化卷积核			高斯卷积核			PSO优化一层卷积核			PSO优化两层卷积核
迭代次数	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%
30	2 558.22	93.10	97.60	2 545.03	95.00	96.00	2 562.22	94.90	99.00	2 570.00	95.50	97.00
40	3 408.20	93.30	98.40	3 395.23	95.60	96.00	3 411.77	95.60	97.40	3 406.02	95.80	97.40
50	4 265.31	94.00	98.40	4 235.45	96.00	97.40	4 273.44	95.80	97.20	4 257.02	96.50	97.80
60	5 116.59	94.90	97.00	5 094.44	96.20	96.80	5 124.09	95.90	96.40	5 109.05	96.70	96.80
70	5 975.69	95.30	96.00	5 963.13	96.40	97.20	6 001.06	96.50	97.20	5 933.23	97.00	99.00

迭代次数	随机化卷积核			高斯卷积核			PSO优化一层卷积核			PSO优化两层卷积核
迭代次数	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score
30	88.60	0.87	0.93	94.00	0.90	0.95	90.80	0.90	0.95	94.00	0.91	0.96
40	88.20	0.87	0.93	95.20	0.91	0.96	93.80	0.91	0.96	94.20	0.92	0.96
50	89.60	0.88	0.94	94.60	0.92	0.96	94.40	0.92	0.96	95.20	0.93	0.97
60	92.80	0.90	0.95	95.60	0.92	0.96	95.40	0.92	0.96	96.60	0.93	0.97
70	94.60	0.91	0.95	95.60	0.93	0.96	95.80	0.93	0.97	95.00	0.94	0.97

批次大小	随机化卷积核			高斯卷积核			PSO优化一层卷积核			PSO优化两层卷积核
批次大小	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%	训练时间/s	识别率/%	灵敏度/%
50	3 404.27	94.40	97.40	3 420.74	95.30	97.20	3 549.56	95.00	97.80	3 608.60	96.10	97.00
100	3 321.73	91.40	99.20	3 329.66	93.10	98.20	3 428.76	92.50	93.20	3 569.05	94.30	98.00
150	3 297.91	90.40	97.60	3 298.88	92.80	97.40	3 324.80	91.10	96.60	3 455.64	93.00	93.00
200	3 199.72	89.10	99.40	3 186.80	90.50	98.60	3 251.77	89.50	100.00	3 358.09	91.60	95.00

批次大小	随机化卷积核			高斯卷积核			PSO优化一层卷积核			PSO优化两层卷积核
批次大小	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score	特异度/%	MCC	F₁Score
50	91.40	0.89	0.94	93.40	0.91	0.95	92.20	0.90	0.95	95.00	0.92	0.96
100	83.60	0.84	0.91	88.00	0.87	0.93	91.80	0.85	0.93	90.00	0.89	0.94
150	83.20	0.82	0.90	88.20	0.86	0.93	85.60	0.83	0.91	93.00	0.86	0.93
200	78.80	0.80	0.89	82.40	0.82	0.90	79.00	0.81	0.89	88.00	0.83	0.92

网络层数	随机化卷积核		高斯卷积核		PSO一卷积核		PSO两卷积核		PSO三卷积核		PSO四卷积核
网络层数	训练时间/s	识别率/%	训练时间/s	识别率/%	训练时间/s	识别率/%	训练时间/s	识别率/%	训练时间/s	识别率/%	训练时间/s	识别率/%
5	3 909.86	92.40	3 901.33	94.60	3 926.79	95.20	4 017.86	96.10
6	4 414.08	93.90	4 232.13	95.00	4 453.02	95.80	4 477.57	96.50	4 496.98	97.00
7	4 471.54	94.20	4 566.62	95.50	4 497.06	96.30	4 507.26	97.20	4 523.96	97.70
8	4 526.35	95.80	4 647.58	96.80	4 545.81	96.70	4 577.11	97.60	4 774.98	98.00	5 6491.26	98.30