Research on convolutional neural network and its application on medical image_Journal of Biomedical Engineering

Authors：

LIANG Mengmeng ¹ ,  ZHOU Tao ^2,3 , ZHANG Feifei ¹ , YANG Jian ¹ , XIA Yong ⁴

1. School of Public Health and Management, Ningxia Medical University, Yinchuan 750004, P.R.China;
2. School of Computer Science and Engineering, North Minzu University, Yinchuan 750004, P.R.China;
3. School of Science, Ningxia Medical University, Yinchuan 750004, P.R.China;
4. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, P.R.China;

Corresponding author：

ZHOU Tao, Email: zhoutaonxmu@126.com

Keywords：

convolutional neural network; network structure; learning algorithm; medical image

DOI：

10.7507/1001-5515.201710060

Video：

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Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.

Citation： LIANG Mengmeng, ZHOU Tao, ZHANG Feifei, YANG Jian, XIA Yong. Research on convolutional neural network and its application on medical image. Journal of Biomedical Engineering, 2018, 35(6): 977-985. doi: 10.7507/1001-5515.201710060 Copy

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6.	杨格兰, 邓晓军, 刘琮. 基于深度时空域卷积神经网络的表情识别模型. 中南大学学报:自然科学版, 2016, 47(7): 2311-2319.
7.	杜昌顺, 黄磊. 分段卷积神经网络在文本情感分析中的应用. 计算机工程与科学, 2017, 39(1): 173-179.
8.	张力, 张洞明, 郑宏. 基于联合层特征的卷积神经网络在车标识别中的应用. 计算机应用, 2016, 36(2): 444-448.
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11.	邬美银, 陈黎, 田菁. 基于卷积神经网络的视频图像失真检测及分类. 计算机应用研究, 2016, 33(9): 2827-2830.
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17.	刘万军, 梁雪剑, 曲海成. 不同池化模型的卷积神经网络学习性能研究. 中国图象图形学报, 2016, 21(9): 1178-1190.
18.	Huang Zhen, Siniscalchi S M, Lee C H. A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition. Neurocomputing, 2016, 218: 448-459.
19.	李雯. 基于深度卷积神经网络的 CT 图像肝脏肿瘤分割方法研究. 中国科学院大学, 深圳: 中国科学院深圳先进技术研究院, 2016.
20.	Shen Wei, Zhou Mu, Yang Feng, et al. Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging, 2015, 24: 588-599.
21.	郝占龙, 罗晓曙, 赵书林. 基于同层多尺度核 CNN 的单细胞图像分类. 计算机工程与应用, 2018, 54(15): 181-184.
22.	Gao X W, Hui Rui, Tian Zengmin. Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed, 2017, 138: 49-56.
23.	Mishra M, Schmitt S, Wang Lichao, et al. Structure-based assessment of cancerous mitochondria using deep networks//2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague: Czech Republic, 2016: 545-548.
24.	秦志光, 陈浩, 丁熠, 等. 基于多模态卷积神经网络的脑血管提取方法研究. 电子科技大学学报, 2016, 45(4): 573-581.
25.	Chen J, Qi X, Tervonen O, et al. Thorax disease diagnosis using deep convolutional neural network//38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC). Orlando, USA, 2016: 2287-2290.

1. Guo Yanming, Liu Yu, Oerlemans A, et al. Deep learning for visual understanding: A review. Neurocomputing, 2016, 187: 27-48.
2. Hayman S. The McCulloch-Pitts model//International Joint Conference on Neural Networks. Washington, USA: IEEE, 1999: 4438-4439.
3. Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
4. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//International Conference on Neural Information Processing Systems. Lake Tahoe, USA: Curran Associates, 2012: 1097-1105.
5. Gökhan. Hazim K E. Initialization of convolutional neural networks by Gabor filters//26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018: 1-4.
6. 杨格兰, 邓晓军, 刘琮. 基于深度时空域卷积神经网络的表情识别模型. 中南大学学报:自然科学版, 2016, 47(7): 2311-2319.
7. 杜昌顺, 黄磊. 分段卷积神经网络在文本情感分析中的应用. 计算机工程与科学, 2017, 39(1): 173-179.
8. 张力, 张洞明, 郑宏. 基于联合层特征的卷积神经网络在车标识别中的应用. 计算机应用, 2016, 36(2): 444-448.
9. 王伟凝, 王励, 赵明权, 等. 基于并行深度卷积神经网络的图像美感分类. 自动化学报, 2016, 42(6): 904-914.
10. 刘占文, 赵祥模, 李强, 等. 基于图模型与卷积神经网络的交通标志识别方法. 交通运输工程学报, 2016, 16(5): 122-131.
11. 邬美银, 陈黎, 田菁. 基于卷积神经网络的视频图像失真检测及分类. 计算机应用研究, 2016, 33(9): 2827-2830.
12. 陈耀丹, 王连明. 基于卷积神经网络的人脸识别方法. 东北师大学报:自然科学版, 2016, 48(2): 70-76.
13. 刘冶, 潘炎, 夏榕楷, 等. FP-CNNH: 一种基于深度卷积神经网络的快速图像哈希算法. 计算机科学, 2016, 43(9): 39-46, 51.
14. 李寰宇, 毕笃彦, 查宇飞, 等. 一种易于初始化的类卷积神经网络视觉跟踪算法. 电子与信息学报, 2016, 38(1): 1-7.
15. Guan Sheng, Chen Min, Ha H Y, et al. Deep learning with MCA-based instance selection and bootstrapping for imbalanced data classification//2015 IEEE Conference On Collaboration And Internet Computing (CIC), Hangzhou, China: IEEE, 2015: 288-295.
16. 吴素雯, 战荫伟. 基于选择性搜索和卷积神经网络的人脸检测. 计算机应用研究, 2017, 34(9): 2854-2857, 2876.
17. 刘万军, 梁雪剑, 曲海成. 不同池化模型的卷积神经网络学习性能研究. 中国图象图形学报, 2016, 21(9): 1178-1190.
18. Huang Zhen, Siniscalchi S M, Lee C H. A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition. Neurocomputing, 2016, 218: 448-459.
19. 李雯. 基于深度卷积神经网络的 CT 图像肝脏肿瘤分割方法研究. 中国科学院大学, 深圳: 中国科学院深圳先进技术研究院, 2016.
20. Shen Wei, Zhou Mu, Yang Feng, et al. Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging, 2015, 24: 588-599.
21. 郝占龙, 罗晓曙, 赵书林. 基于同层多尺度核 CNN 的单细胞图像分类. 计算机工程与应用, 2018, 54(15): 181-184.
22. Gao X W, Hui Rui, Tian Zengmin. Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed, 2017, 138: 49-56.
23. Mishra M, Schmitt S, Wang Lichao, et al. Structure-based assessment of cancerous mitochondria using deep networks//2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague: Czech Republic, 2016: 545-548.
24. 秦志光, 陈浩, 丁熠, 等. 基于多模态卷积神经网络的脑血管提取方法研究. 电子科技大学学报, 2016, 45(4): 573-581.
25. Chen J, Qi X, Tervonen O, et al. Thorax disease diagnosis using deep convolutional neural network//38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC). Orlando, USA, 2016: 2287-2290.

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