Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (5): 77-84.doi: 10.6040/j.issn.1672-3961.0.2018.191

• Control Science & Engineering • Previous Articles     Next Articles

Lung tumor images recognition based on PSO-ConvK convolutional neural network

Mengmeng LIANG1(),Tao ZHOU1,2,*(),Yong XIA3,Feifei ZHANG1,Jian YANG1   

  1. 1. School of Public Health and Management, Ningxia Medical University, Yinchuan 750004, Ningxia, China
    2. School of Science, Ningxia Medical University, Yinchuan 750004, Ningxia, China
    3. School of Computer Science, Northwestern Polytechnical University, Xi′an 710072, Shaanxi, China
  • Received:2018-05-31 Online:2018-10-01 Published:2018-05-31
  • Contact: Tao ZHOU E-mail:1020881411@qq.com;zhoutaonxmu@126.com
  • Supported by:
    国家自然科学基金资助项目(61561040);陕西省教育厅资助项目(2013JK1142)

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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

CLC Number: 

  • TP183

Fig.1

Convolutional neural network structure"

Fig.2

Algorithm framework"

Fig.3

Flow chart of PSO combined with Gradient descent method"

Fig.4

Pre-processed experimental data"

Table 1

Comparison of recognition rate and sensitivity of PSO optimized convolution kernel with iteration number"

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
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

Table 2

Comparison of specificity, MCC and F1Score of PSO optimized convolution kernel with iteration times"

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
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

Table 3

Comparison of recognition rate and sensitivity of PSO optimized convolution kernel with batch sizes"

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
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

Table 4

Comparison of specificity, MCC and F1Score of PSO optimized convolution kernel with batch sizes"

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
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

Table 5

Comparison of recognition rate of PSO optimized convolution kernel with network layers"

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
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

Table 6

Comparison of sensitivity and specificity of PSO optimized convolution kernel with network layers"

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
灵敏度/% 特异度/% 灵敏度/% 特异度/% 灵敏度/% 特异度/% 灵敏度/% 特异度/% 灵敏度/% 特异度/% 灵敏度/% 特异度/%
5 89.40 95.40 99.20 90.00 98.40 92.00 96.80 95.40
6 99.40 88.40 93.60 96.40 98.60 93.00 96.00 97.00 98.20 95.80
7 92.20 96.20 96.40 94.60 99.80 92.80 98.00 96.40 99.00 96.40
8 94.80 96.80 99.00 94.40 98.40 95.00 98.00 97.20 98.60 97.40 100.00 96.60

Table 7

Comparison of MCC and F1Score of PSO optimized convolution kernel with network layers"

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
MCC F1Score MCC F1Score MCC F1Score MCC F1Score MCC F1Score MCC F1Score
5 0.85 0.92 0.90 0.95 0.91 0.95 0.92 0.96
6 0.88 0.94 0.90 0.95 0.92 0.96 0.93 0.97 0.94 0.97
7 0.88 0.94 0.91 0.96 0.93 0.96 0.94 0.97 0.95 0.98
8 0.92 0.96 0.94 0.97 0.93 0.97 0.95 0.98 0.96 0.98 0.97 0.98
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