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.

图/表 11

图1

图2

图3

图4

表1

表2

表3

表4

表5

表6

表7

参考文献 20

1	陈万青, 郑荣寿, 张思维, 等. 2012年中国恶性肿瘤发病和死亡分析[J]. 中国肿瘤, 2016, 25 (1): 1- 8. doi: 10.3969/j.issn.1674-4136.2016.01.001
	CHEN Wanqing , ZHENG Rongshou , ZHANG Siwei , et al. Analysis of the incidence and death of malignant tumors in China in 2012[J]. Bulletin of China, 2016, 25 (1): 1- 8. doi: 10.3969/j.issn.1674-4136.2016.01.001
2	张忠凤, 张春. 多层螺旋CT对肺癌的诊断价值[J]. 实用临床医学, 2016, 17 (1): 53- 54.
	ZHANG Zhongfeng , ZHANG Chun . Multi-slice spiral CT in diagnosis of lung cancer[J]. Practical Clinical Medicine, 2016, 17 (1): 53- 54.
3	常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42 (9): 1300- 1312.
	CHANG Liang , DENG Xiaoming , ZHOU Mingquan , et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 2016, 42 (9): 1300- 1312.
4	HU Hong , PANG Liang , SHI Zhongzhi . Image matting in the perception granular deep learning[J]. Knowledge-Based Systems, 2016, 102, 51- 63. doi: 10.1016/j.knosys.2016.03.018
5	余萍, 赵继生. 基于矩阵2-范数池化的卷积神经网络图像识别算法[J]. 图学学报, 2016, 39 (5): 694- 701.
	YU Ping , ZHAO Jisheng . Convolution neural network image recognition algorithm based on matrix 2-norm pooling[J]. Journal of Graphics, 2016, 39 (5): 694- 701.
6	GRINBLAT Guillermo , UZAL Lucas , LARESE Mónica , et al. Deep learning for plant identification using vein morphological patterns[J]. Computers & Electronics in Agriculture, 2016, 127, 418- 424.
7	ISLAM Jyoti, ZHANG Yanqing. Visual sentiment analysis for social images using transfer learning approach[C]// Processdings of IEEE International Conferences on Big Data and Cloud Computing. Washington, USA: IEEE, 2016: 124-130.
8	ZHAO Wenzhi , DU Shihong . Learning multiscale and deep representations for classifying remotely sensed imagery[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2016, 113, 155- 165.
9	GAO X W , HUI R , Tian Z . Classification of CT brain images based on deep learning networks[J]. Computer Methods & Programs in Biomedicine, 2017, 138, 49- 56.
10	CHEN Jie, QI Xianbiao, TERVONEN O, et al. Thorax disease diagnosis using deep convolutional neural network[C] // Processdings of Engineering in Medicine and Biology Society. Ottawa, Canada: IEEE, 2016: 2287- 2290.
11	MISHRA Manish, SCHMITT Sabine, WANG Lichao, et al. Structure-based assessment of cancerous mitochondria using deep networks[C]// Processdings of IEEE, International Symposium on Biomedical Imaging. Prague, Czech Republic: IEEE, 2016: 545-548.
12	王媛媛, 周涛, 陆惠玲, 等. 基于集成卷积神经网络的肺部肿瘤计算机辅助诊断模型[J]. 生物医学工程学杂志, 2017, 34 (4): 543- 551.
	WANG Yuanyuan , ZHOU Tao , LU Huiling , et al. Computer-aided diagnosis model of lung cancer based on integrated convolutional neural network[J]. Journal of biomedical engineering, 2017, 34 (4): 543- 551.
13	赵志刚, 林玉娇, 尹兆远. 基于自适应惯性权重的均值粒子群优化算法[J]. 计算机工程与科学, 2016, 38 (3): 501- 506. doi: 10.3969/j.issn.1007-130X.2016.03.016
	ZHAO Zhigang , LIN Yujiao , YIN Zhaoyuan . A mean particle swarm optimization algorithm based on adaptive inertia weight[J]. Computer Engineering & Science, 2016, 38 (3): 501- 506. doi: 10.3969/j.issn.1007-130X.2016.03.016
14	LAN Zhenzhong , YU Shooul , LIN Ming , et al. Handcrafted local features are convolutional neural networks[J]. Computer Science, 2015, (3): 92411- 92419.
15	OH Beonseok , OH Kangrok . A gabor-based network for heterogeneous face recognition[J]. Neurocomputing, 2017, 261, 253- 265. doi: 10.1016/j.neucom.2015.11.137
16	BALASARASWATHI M , KALPANA B . Fast and effective classification using parallel and multi-start PSO[J]. Journal of Information Technology Research, 2018, 11 (2): 13- 30. doi: 10.4018/JITR
17	YE Fei . Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data[J]. Plos One, 2017, 12 (12): 152- 188.
18	王媛媛.基于卷积神经网络的肺部肿瘤PET/CT计算机辅助诊断研究[D].宁夏医科大学, 2017.
	WANG Yuanyuan. Computer aided diagnosis of lung tumors PET/CT based on convolution neural network[D]. Ningxia Medical University, 2017.
19	CHEN Junwen , LIU Zhigang , WANG Hongrui , et al. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network[J]. IEEE Transactions on Instrumentation & Measurement, 2018, (99): 1- 13.
20	ZHOU Honglu , SONG Mingli , PEDRYCZ Witold . A comparative study of improved GA and PSO in solving multiple traveling salesmen problem[J]. Applied Soft Computing, 2018, 64, 564- 580. doi: 10.1016/j.asoc.2017.12.031

相关文章 15

[1]	董明书,陈俐企,马川义,张珠皓,孙仁娟,管延华,庄培芝. 沥青路面内部裂缝雷达图像智能判识算法研究[J]. 山东大学学报 (工学版), 2025, 55(3): 72-79.
[2]	李伟豪,王苹苹,许万博,魏本征. 结构先验引导的多模态腰椎MRI图像分割算法[J]. 山东大学学报 (工学版), 2025, 55(1): 66-76.
[3]	刘全金,嵇文,胡浪涛,黄汇磊,杨瑞,李翔,高泽文,魏本征. 基于双解码器的医学图像分割模型[J]. 山东大学学报 (工学版), 2024, 54(6): 8-18.
[4]	杜睿山,井远光,孟令东,张豪鹏. 基于改进多目标粒子群算法的储气库注气优化[J]. 山东大学学报 (工学版), 2024, 54(4): 42-50.
[5]	宋辉,张轶哲,张功萱,孟元. 基于类权重和最小化预测熵的测试时集成方法[J]. 山东大学学报 (工学版), 2024, 54(3): 36-43.
[6]	马翔悦,徐金东,倪梦莹. 基于多尺度特征模糊卷积神经网络的遥感图像分割[J]. 山东大学学报 (工学版), 2024, 54(3): 44-54.
[7]	孙园,曾惠权,欧阳苏建,高佳倩,王绮楠,林智勇. 基于粒子群算法的模糊大脑情感学习非线性系统辨识[J]. 山东大学学报 (工学版), 2024, 54(1): 25-32.
[8]	迟云浩,杨璐,郭杰,郝凡昌,聂秀山. 基于注意力特征融合网络的手指静脉图像质量评价方法[J]. 山东大学学报 (工学版), 2023, 53(6): 56-62.
[9]	那绪博,张莹,李沐阳,陈元畅,华云鹏. 基于ODCG的网约车需求预测模型[J]. 山东大学学报 (工学版), 2023, 53(5): 48-56.
[10]	范海雯,郝旭东,赵康,邢法财,蒋哲,李常刚. 基于卷积神经网络的含分布式光伏配电网静态等值[J]. 山东大学学报 (工学版), 2023, 53(4): 140-148.
[11]	王智伟,徐海超,郭相阳,马炯,褚云龙,陈前昌,卢治. 基于卷积神经网络和层次分析的新能源电源调频能力智能预测方法[J]. 山东大学学报 (工学版), 2022, 52(5): 70-76.
[12]	黄彩云,陈德武,何吉福,胡艺,王楠,陈沛. 基于改进双路径网络的上肢肌肉骨骼异常检测[J]. 山东大学学报 (工学版), 2022, 52(3): 25-33.
[13]	张学思,张婷,刘兆英,江天鹏. 基于轻量型卷积神经网络的海面红外显著性目标检测方法[J]. 山东大学学报 (工学版), 2022, 52(2): 41-49.
[14]	王心哲,邓棋文,王际潮,范剑超. 深度语义分割MRF模型的海洋筏式养殖信息提取[J]. 山东大学学报 (工学版), 2022, 52(2): 89-98.
[15]	尹旭,刘兆英,张婷,李玉鑑. 基于弱监督和半监督学习的红外舰船分割方法[J]. 山东大学学报 (工学版), 2022, 52(2): 99-106.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

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