Abstract
Pneumoconiosis is one of the most serious occupational diseases in China, which seriously endangers the health of most workers in dust environments. The diagnosis of pneumoconiosis is very complex and cumbersome, which relies mostly on doctor’s medical knowledge and clinical reading experiences of X-ray chest film. Traditional image processing approach has helped doctors to reduce the misdiagnosis but with lower accuracy. An improved CNN-based pneumoconiosis diagnosis method on X-ray chest films is proposed to predict pneumoconiosis disease. The CNN structure is decomposed from \(5\times 5\) convolution kernel into two \(3\times 3\) convolution kernels to optimize the execution. Compared with GoogLeNet, the proposed GoogLeNet-CF achieves higher accuracy and gives a good result in the diagnosis of pneumoconiosis disease.
Supported by the project of medical and health big data center from Hubei Provincial Development and Reform Commission.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schenker, M., Pinkerton, K., Mitchell, D., Vallyathan, V., Elvine, B., Green, B.: Pneumoconiosis from agricultural dust exposure among young California farmworkers. Environ. Health Perspect. 117(6), 988 (2009)
Melle, W.V.: MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int. J. Man Mach. Stud. 10(3), 313–322 (1978)
Kraus, T., Raithel, H., Lehnert, G.: Computer-assisted classification system for chest X-ray and computed tomography findings in occupational lung disease. Int. Arch. Occup. Environ. Health 69(6), 182–486 (1997)
Roth, H., et al.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016)
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
Tajbakhsh, N., Gotway, M.B., Liang, J.: Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 62–69. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_8
Powles, J., Hodson, H.: Google DeepMind and healthcare in an age of algorithms. Health Technol. 7(4), 351–367 (2017)
Liu, X., Hou, F., Qin, H., Hao, A.: A CADe system for nodule detection in thoracic CT images based on artificial neural network. Sci. China (Inf. Sci.) 60(7), 072106 (2017)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia (MM), pp. 675–678. ACM, USA (2014)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE, USA (2015)
Chen, T., et al.: MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems. Comput. Sci. 6(2), 2433–2445 (2015)
Ku, C., Lee, K.: Diagonal recurrent neural networks for dynamic systems control. IEEE Trans. Neural Netw. 6(1), 144–156 (1995)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. IEEE, USA (2015)
Hei, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, USA (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826. IEEE, USA (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, R., Deng, K., Jin, H., Liu, H., Zhang, L. (2019). An Improved CNN-Based Pneumoconiosis Diagnosis Method on X-ray Chest Film. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_66
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)