Pulmonary nodule detection method based on convolutional neural network_Journal of Biomedical Engineering

Authors：

LIU Yiming , HOU Zhichao ,  LI Xiaoqin , WANG Xuedong

College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, P.R.China;

Corresponding author：

LI Xiaoqin, Email: lxq0811@bjut.edu.cn

Keywords：

computed tomography; computer-aided detection; lung nodules; convolutional neural network; cross validation

DOI：

10.7507/1001-5515.201902001

Video：

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A method was proposed to detect pulmonary nodules in low-dose computed tomography (CT) images by two-dimensional convolutional neural network under the condition of fine image preprocessing. Firstly, CT image preprocessing was carried out by image clipping, normalization and other algorithms. Then the positive samples were expanded to balance the number of positive and negative samples in convolutional neural network. Finally, the model with the best performance was obtained by training two-dimensional convolutional neural network and constantly optimizing network parameters. The model was evaluated in Lung Nodule Analysis 2016(LUNA16) dataset by means of five-fold cross validation, and each group's average model experiment results were obtained with the final accuracy of 92.3%, sensitivity of 92.1% and specificity of 92.6%.Compared with other existing automatic detection and classification methods for pulmonary nodules, all indexes were improved. Subsequently, the model perturbation experiment was carried out on this basis. The experimental results showed that the model is stable and has certain anti-interference ability, which could effectively identify pulmonary nodules and provide auxiliary diagnostic advice for early screening of lung cancer.

Citation： LIU Yiming, HOU Zhichao, LI Xiaoqin, WANG Xuedong. Pulmonary nodule detection method based on convolutional neural network. Journal of Biomedical Engineering, 2019, 36(6): 969-977. doi: 10.7507/1001-5515.201902001 Copy

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15.	Bishop C. Training with noise is equivalent to Tikhonov regularization. Neural Comput, 1995, 7(1): 108-116.
16.	Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25(NIPS 2012), Curran Associates Inc. 2012: 1097-1105.
17.	Wang Shuo, Zhou Mu, Liu Zaiyi, et al. Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med Image Anal, 2017, 40: 172-183.
18.	Dou Qi, Chen Hao, Yu Lequan, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
19.	李飞腾. 卷积神经网络及其应用. 大连: 大连理工大学, 2014: 20-25.
20.	Murphy K, van Ginneken B, Schilham A, et al. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal, 2009, 13(5): 757-770.
21.	吕晓琪, 吴凉, 谷宇, 等. 基于三维卷积神经网络的低剂量 CT 肺结节检测. 光学精密工程, 2018, 26(5): 1211-1218.
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1. Siegel R L, Miller K D, Jemal A. Cancer statistics. CA: A Cancer Journal for Clinicians, 2015, 65: 5-29.
2. Hu Baotian, Lu Zhengdong, Li Hang, et al. Convolutional neural network architectures for matching natural language sentences. Advances in neural information processing systems 3, 2015. arXiv: 1503.03244.
3. Armato I S, Roberts R Y, Mcnitt-Gray M F, et al. The lung image database consortium (LIDC): ensuring the integrity of expert-defined " truth”. Acad Radiol, 2007, 14(12): 1455-1463.
4. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol, 2017, 10(3): 257-273.
5. Anirudhi R, Thiagarajan J J, Bremer T, et al. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data//Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, 2016, 9785: 978532.
6. Liu Jikui, Jiang Hongyang, Gao Mengdi, et al. An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. J Med Syst, 2017, 41(2): 1-9.
7. Shen Wei, Zhou Mu, Yang Feng, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit, 2017, 61: 663-673.
8. Kumar D, Wong A, Clausi D A. Lung nodule classification using deep features in CT images//2015 12th Conference on Computer and Robot Vision (Crv 2015), 2015: 133-138.
9. Hua K L, Hsu C H, Hidayati H C, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8: 2015-2022.
10. Srivastava N, Hinton G, Krizhevsky A A, et al. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
11. Setio A A, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
12. 孙少燕. 基于像素灰度的医学图像刚性配准方法研究. 大连: 大连理工大学, 2007.
13. Lumsden A B, Heyman E R. Prospective randomized study evaluating an absorbable cyanoacrylate for use in vascular reconstructions. J Vasc Surg, 2006, 44(5): 1002-1009.
14. Choi W J, Choi T S. Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Information Sciences, 2012, 212(2): 57-78.
15. Bishop C. Training with noise is equivalent to Tikhonov regularization. Neural Comput, 1995, 7(1): 108-116.
16. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25(NIPS 2012), Curran Associates Inc. 2012: 1097-1105.
17. Wang Shuo, Zhou Mu, Liu Zaiyi, et al. Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med Image Anal, 2017, 40: 172-183.
18. Dou Qi, Chen Hao, Yu Lequan, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
19. 李飞腾. 卷积神经网络及其应用. 大连: 大连理工大学, 2014: 20-25.
20. Murphy K, van Ginneken B, Schilham A, et al. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal, 2009, 13(5): 757-770.
21. 吕晓琪, 吴凉, 谷宇, 等. 基于三维卷积神经网络的低剂量 CT 肺结节检测. 光学精密工程, 2018, 26(5): 1211-1218.
22. 金奇樑. 基于CT图像的肺结节自动识别系统研究. 杭州: 浙江大学, 2016: 40-50.
23. Dandil E, Çakiroğlu M, Ekşi Z, et al. Artificial neural network-based classification system for lung nodules on computed tomography scans//2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), IEEE, 2014: 382-386.

Journal of Biomedical Engineering

Pulmonary nodule detection method based on convolutional neural network

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