Abstract
In this paper, we present a deep learning based approach for automatically detecting tuberculosis manifestation from chest X-ray images. India is the country with the highest burden of tuberculosis. A chest radiograph in symptomatic patients is used to diagnose active tuberculosis. This screening method is ideally done at the primary health care centres where a clinician is available and sometimes through mobile X-ray unit. The major challenge for this method of screening is timely reporting and further follow-up of patient for initiation of treatment. We built multiple convolutional neural networks, the state-of-the-art deep learning algorithm, to build the model for automatic tuberculosis diagnosis. We classified the chest X-rays into two categories, namely, tuberculosis presence and tuberculosis absence. The dataset used to train the model contained 678 images, having 340 normal chest X-rays and 338 chest X-rays with tuberculosis manifestation. The validation dataset contained 235 images, which observed a sensitivity of 84.91% and a specificity of 93.02%. This demonstrates the potential of convolutional neural networks to automatically classify chest X-rays in real time.
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
References
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.
He, K., Zhang, X., Ren, S., & Sun, J. (October 2016a). Identity mappings in deep residual networks. In European Conference on Computer Vision (pp. 630–645). Springer International Publishing.
He, K., Zhang, X., Ren, S., & Sun, J. (2016b). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
Ioffe, S., & Szegedy, C. (June 2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning (pp. 448–456).
Jaeger, S., Karargyris, A., Antani, S., & Thoma, G. (August 2012). Detecting tuberculosis in radiographs using combined lung masks. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 4978–4981). IEEE.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., et al. (2014). Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging, 33(2), 233–245.
Krizhevsky, A., Sutskever, I., & Hinton, G. (2012) Imagenet classification with deep convolutional neural networks. In NIPS.
LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., et al. (1990). Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (pp. 396–404).
Murtagh, K. A. T. H. L. E. E. N. (1980). Unreliability of the Mantoux test using 1 TU PPD in excluding childhood tuberculosis in Papua New Guinea. Archives of Disease in Childhood, 55(10), 795–799.
Perkins, M. D. (2000). New diagnostic tools for tuberculosis [The Eddie O’Brien Lecture]. The International Journal of Tuberculosis and Lung Disease, 4(12), S182–S188.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–9).
Van Cleeff, M. R. A., Kivihya-Ndugga, L. E., Meme, H., Odhiambo, J. A., & Klatser, P. R. (2005). The role and performance of chest X-ray for the diagnosis of tuberculosis: a cost-effectiveness analysis in Nairobi. Kenya. BMC Infectious Diseases, 5(1), 111.
Van Ginneken, B., Katsuragawa, S., ter Haar Romeny, B. M., Doi, K., & Viergever, M. A. (2002). Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging, 21(2), 139–149.
World Health Organization, WHO (2016). Global Tuberculosis Report 2016. http://www.who.int/tb/publications/global_report/en/.
Yang, J., Yu, K., Gong, Y., & Huang, T. (June 2009). Linear spatial pyramid matching using sparse coding for image classification. In IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. (pp. 1794–1801). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Raju, M., Aswath, A., Kadam, A., Pagidimarri, V. (2019). Automatic Detection of Tuberculosis Using Deep Learning Methods. In: Laha, A. (eds) Advances in Analytics and Applications. Springer Proceedings in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-13-1208-3_11
Download citation
DOI: https://doi.org/10.1007/978-981-13-1208-3_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1207-6
Online ISBN: 978-981-13-1208-3
eBook Packages: Business and ManagementBusiness and Management (R0)