A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis_Journal of Biomedical Engineering

Authors：

WANG Yu ¹ ,  WU Jiang ² , WU Dongsheng ³

1. Department of Biomedical Engineering , College of Biomedical Engineering, Sichuan University, Chengdu 610041, P. R. China;
2. Deep Underground Space Medical Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. Department of Radiology, West China Fourth Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

Keywords：

Pneumoconiosis; Imaging diagnosis; Deep learning; Convolutional neural network

DOI：

10.7507/1001-5515.202309079

Video：

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Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.

Citation： WANG Yu, WU Jiang, WU Dongsheng. A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis. Journal of Biomedical Engineering, 2024, 41(2): 413-420. doi: 10.7507/1001-5515.202309079 Copy

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4.	国家质检总局. 尘肺X线诊断标准及处理原则: GB5906-1986. 北京: 中国标准出版社, 1986.
5.	乔鹏飞, 杨军, 苏秉亮, 等. 尘肺病的影像学诊断现状及展望. 内蒙古医学杂志, 2006, 38(8): 742-744.
6.	郑光远, 刘峡壁, 韩光辉. 医学影像计算机辅助检测与诊断系统综述. 软件学报, 2018, 29(5): 1471-1514.
7.	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
8.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
9.	周涛, 霍兵强, 陆惠玲, 等. 医学影像疾病诊断的残差神经网络优化算法研究进展. 中国图象图形学报, 2020, 25(10): 2079-2092.
10.	汪伟. 基于深度卷积神经网络的尘肺病DR影像诊断研究. 唐山: 华北理工大学, 2021.
11.	Zhang X Y, Zhou X Y, Lin M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6848-6856.
12.	Zheng R, Zhang L L, Jin H. Pneumoconiosis identification in chest X-ray films with CNN-based transfer learning. CCF Trans HPC, 2021, 3: 186-200.
13.	崔风涛, 王研, 丁新平, 等. 一种轻量级卷积神经网络在煤工尘肺早期阶段自动识别中的应用. 中华劳动卫生职业病杂志, 2023, 41(3): 177-182.
14.	Wang Y, Cui F T, Ding X P. Automated identification of the preclinical stage of coal workers’ pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomedical Signal Processing and Control, 2023, 83: 104607.
15.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv, 2014, 2014: 1409-1556.
16.	赵奇, 郝超凡, 邱翠娟, 等. 基于视觉几何组卷积神经网络的尘肺病诊断初探. 中国工业医学杂志, 2021, 34(6): 564-566,577.
17.	舒甜督, 刘芳, 蔡茂. 基于卷积神经网络的肺部 CT 图像分类算法研究. 电子设计工程, 2022, 30(21): 170-174,179.
18.	Wu S, Li G, Deng L, et al. L1- norm batch normalization for efficient training of Deep Neural Networks. IEEE Trans Neur Netw Learn Syst, 2019, 30(7): 2043-2051.
19.	杨云, 张立泽清, 齐勇, 等. 基于残差网络的血管内超声图像识别. 计算机仿真, 2020, 37(4): 269-273.
20.	Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
21.	张雅娟. 尘肺影像分期一致性研究与自动分期模型构建. 广州: 南方医科大学, 2021.
22.	Fan L, Wang Z, Zhou J. LDADN: a local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection. Biomed Opt Express, 2022, 13(8): 4353-4369.
23.	Rajpurkar P, Irvin J, Zhu K, et a1. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv, 2017, 2017: 1711.05225v2.
24.	Devnath L, Luo S, Summons P, et al. Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs. Comput Biol Med, 2021, 129: 104125.
25.	徐继伟, 杨云. 集成学习方法: 研究综述. 云南大学学报(自然科学版), 2018, 40(6): 1082-1092.
26.	Devnath L, Luo S, Summons P, et al. Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker’s chest X-ray radiography. J Clin Med, 2022, 11(18): 5342.
27.	Christian S, Vincent V, Sergey I, et al. Rethinking the inception architecture for computer vision. arXiv, 2015, 2015: 1512.00567v3.
28.	张兰兰. 基于迁移学习的尘肺病胸片分析与识别. 武汉: 华中科技大学, 2020.
29.	庄福振, 罗平, 何清, 等. 迁移学习研究进展. 软件学报, 2015, 26(1): 26-39.
30.	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// Navab N, Hornegger J, Wells W, et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Cham: Springer, 2015, 9351: 234-241.
31.	王峥, 钱青俊, 张建芳, 等. 计算机辅助诊断在尘肺病诊断中应用价值. 中国职业医学, 2020, 47(4): 428-431.
32.	Yang F, Tang Z R, Chen J, et al. Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning. BMC Med Imaging, 2021, 21(1): 189.
33.	Zhang L, Rong R, Li Q, et al. A deep learning-based model for screening and staging pneumoconiosis. Sci Rep, 2021, 11(1): 2201.
34.	石鸣鸣. 基于Inception-ResNet-v2的肺部影像分类方法研究. 广州: 广东工业大学, 2021.
35.	Lumini A. Comparison of different image data augmentation approaches. J Imaging, 2021, 7: 254.
36.	Chlap P, Min H, Vandenberg N, et a1. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol, 2021, 65(5): 545-546.
37.	黄晓鸣, 何富运, 唐晓虎, 等. U-Net及其变体在医学图像分割中的应用研究综述. 中国生物医学工程学报, 2022, 41(5): 567-576.
38.	Zhou P, Chen H J, Yu Z K, et al. Review of cross-modality medical image prediction. Acta Electronica Sinica, 2019, 47(1): 220-226.
39.	Hu X, Zhou R, Hu M, et al. Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network. Comput Methods Programs Biomed, 2022, 225: 107098.
40.	Guo S, Wang G L, Han X. COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter. Biomed Signal Proces, 2022, 75: 103552.
41.	王成霞, 王宁宁, 仇路, 等. 尘肺病胸部 CT 与 DR 胸片影像差异研究. 职业与健康, 2021, 37(1): 5-10.
42.	刘瑞珍, 王峥, 钱青俊, 等. 两种尘肺病计算机智能诊断系统的性能评价. 智慧健康, 2023, 9(7): 1-5.
43.	肖淑玉, 高静, 孙志谦, 等. 多层感知器神经网络模型对职业性煤工尘肺发病预测研究. 中国职业医学, 2021, 48(1): 19-25.

1. 中华人民共和国国家卫生和计划生育委员会. 职业性尘肺病的诊断: GBZ 70—2015. 北京: 中国标准出版社, 2016.
2. 中华人民共和国国家卫生健康委员会. 2021年我国卫生健康事业发展统计公报. (2022-07-12) [2023-09-28]. https://www.gov.cn/xinwen/2022-07/12/content_5700670.htm.
3. 李涛, 张建芳, 孟祥峰, 等. 尘肺病数据标注规范与质量控制专家共识(2020年版). 环境与职业医学, 2020, 37(6): 523-529.
4. 国家质检总局. 尘肺X线诊断标准及处理原则: GB5906-1986. 北京: 中国标准出版社, 1986.
5. 乔鹏飞, 杨军, 苏秉亮, 等. 尘肺病的影像学诊断现状及展望. 内蒙古医学杂志, 2006, 38(8): 742-744.
6. 郑光远, 刘峡壁, 韩光辉. 医学影像计算机辅助检测与诊断系统综述. 软件学报, 2018, 29(5): 1471-1514.
7. 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
8. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
9. 周涛, 霍兵强, 陆惠玲, 等. 医学影像疾病诊断的残差神经网络优化算法研究进展. 中国图象图形学报, 2020, 25(10): 2079-2092.
10. 汪伟. 基于深度卷积神经网络的尘肺病DR影像诊断研究. 唐山: 华北理工大学, 2021.
11. Zhang X Y, Zhou X Y, Lin M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6848-6856.
12. Zheng R, Zhang L L, Jin H. Pneumoconiosis identification in chest X-ray films with CNN-based transfer learning. CCF Trans HPC, 2021, 3: 186-200.
13. 崔风涛, 王研, 丁新平, 等. 一种轻量级卷积神经网络在煤工尘肺早期阶段自动识别中的应用. 中华劳动卫生职业病杂志, 2023, 41(3): 177-182.
14. Wang Y, Cui F T, Ding X P. Automated identification of the preclinical stage of coal workers’ pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomedical Signal Processing and Control, 2023, 83: 104607.
15. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv, 2014, 2014: 1409-1556.
16. 赵奇, 郝超凡, 邱翠娟, 等. 基于视觉几何组卷积神经网络的尘肺病诊断初探. 中国工业医学杂志, 2021, 34(6): 564-566,577.
17. 舒甜督, 刘芳, 蔡茂. 基于卷积神经网络的肺部 CT 图像分类算法研究. 电子设计工程, 2022, 30(21): 170-174,179.
18. Wu S, Li G, Deng L, et al. L1- norm batch normalization for efficient training of Deep Neural Networks. IEEE Trans Neur Netw Learn Syst, 2019, 30(7): 2043-2051.
19. 杨云, 张立泽清, 齐勇, 等. 基于残差网络的血管内超声图像识别. 计算机仿真, 2020, 37(4): 269-273.
20. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
21. 张雅娟. 尘肺影像分期一致性研究与自动分期模型构建. 广州: 南方医科大学, 2021.
22. Fan L, Wang Z, Zhou J. LDADN: a local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection. Biomed Opt Express, 2022, 13(8): 4353-4369.
23. Rajpurkar P, Irvin J, Zhu K, et a1. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv, 2017, 2017: 1711.05225v2.
24. Devnath L, Luo S, Summons P, et al. Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs. Comput Biol Med, 2021, 129: 104125.
25. 徐继伟, 杨云. 集成学习方法: 研究综述. 云南大学学报(自然科学版), 2018, 40(6): 1082-1092.
26. Devnath L, Luo S, Summons P, et al. Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker’s chest X-ray radiography. J Clin Med, 2022, 11(18): 5342.
27. Christian S, Vincent V, Sergey I, et al. Rethinking the inception architecture for computer vision. arXiv, 2015, 2015: 1512.00567v3.
28. 张兰兰. 基于迁移学习的尘肺病胸片分析与识别. 武汉: 华中科技大学, 2020.
29. 庄福振, 罗平, 何清, 等. 迁移学习研究进展. 软件学报, 2015, 26(1): 26-39.
30. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// Navab N, Hornegger J, Wells W, et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Cham: Springer, 2015, 9351: 234-241.
31. 王峥, 钱青俊, 张建芳, 等. 计算机辅助诊断在尘肺病诊断中应用价值. 中国职业医学, 2020, 47(4): 428-431.
32. Yang F, Tang Z R, Chen J, et al. Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning. BMC Med Imaging, 2021, 21(1): 189.
33. Zhang L, Rong R, Li Q, et al. A deep learning-based model for screening and staging pneumoconiosis. Sci Rep, 2021, 11(1): 2201.
34. 石鸣鸣. 基于Inception-ResNet-v2的肺部影像分类方法研究. 广州: 广东工业大学, 2021.
35. Lumini A. Comparison of different image data augmentation approaches. J Imaging, 2021, 7: 254.
36. Chlap P, Min H, Vandenberg N, et a1. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol, 2021, 65(5): 545-546.
37. 黄晓鸣, 何富运, 唐晓虎, 等. U-Net及其变体在医学图像分割中的应用研究综述. 中国生物医学工程学报, 2022, 41(5): 567-576.
38. Zhou P, Chen H J, Yu Z K, et al. Review of cross-modality medical image prediction. Acta Electronica Sinica, 2019, 47(1): 220-226.
39. Hu X, Zhou R, Hu M, et al. Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural network. Comput Methods Programs Biomed, 2022, 225: 107098.
40. Guo S, Wang G L, Han X. COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter. Biomed Signal Proces, 2022, 75: 103552.
41. 王成霞, 王宁宁, 仇路, 等. 尘肺病胸部 CT 与 DR 胸片影像差异研究. 职业与健康, 2021, 37(1): 5-10.
42. 刘瑞珍, 王峥, 钱青俊, 等. 两种尘肺病计算机智能诊断系统的性能评价. 智慧健康, 2023, 9(7): 1-5.
43. 肖淑玉, 高静, 孙志谦, 等. 多层感知器神经网络模型对职业性煤工尘肺发病预测研究. 中国职业医学, 2021, 48(1): 19-25.

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A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis

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