Skip to main content

Deep Learning-Based Software Tools for Tuberculosis Detection in Chest X-Ray Images

  • Conference paper
  • First Online:
Data Engineering and Intelligent Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1407))

  • 480 Accesses

Abstract

Deep learning is a rising phenomenon in data analysis and one of the ten innovative technologies. Deep learning allows for multi-layered computational models to learn data representations with multiple levels of abstraction. Deep learning provides the best solution to visual object recognition. Deep learning offers the best solution to tuberculosis (TB) detection in X-ray images. In a shortage of qualified radiologists, these new technologies increase the capacity to improve overall TB diagnosis and treatment. This paper aims to review deep learning-based software techniques, such as CAD4TB, qXR, and Lunit INSIGHT used for detecting chest X-ray (CXR) abnormalities. Deep learning-based CAD4TB software highlights the abnormal region in the form of a heat map image. qXR software identifies 15 abnormalities from abnormal chest X-ray images. Lunit INSIGHT CXR software identifies ten abnormalities from chest radiography. The human observer must validate deep learning-based computer-aided detection (CAD) systems used in real diagnosis service. The accuracy of CAD software is analysed by using the receiver operating characteristic (ROC) curve. Deep learning-based systems in medicine must meet ethical values and validate the results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. K. Paquette, M.P. Cheng, M.J. Kadatz, V.J. Cook, W. Chen, J.C. Johnston, Chest radiography for active tuberculosis case finding in the homeless: a systematic review and meta-analysis. Int. J. Tuberc. Lung Dis. 18(10), 1231–1236 (2014). https://doi.org/10.5588/ijtld.14.0105

    Article  Google Scholar 

  2. J.Y. Wu et al., The role of chest radiography in the suspicion for and diagnosis of pulmonary tuberculosis in intensive care units. Int. J. Tuberc. Lung Dis. 13(11), 1380–1386 (2009)

    Google Scholar 

  3. C. Schaefer-Prokop, U. Neitzel, H.W. Venema, M. Uffmann, M. Prokop, Digital chest radiography: an update on modern technology, dose containment and control of image quality. Eur. Radiol. 18(9), 1818–1830 (2008). https://doi.org/10.1007/s00330-008-0948-3

    Article  Google Scholar 

  4. M. Bigelow et al., Integrating AI into radiology workflow: levels of research, production, and feedback maturity 7(1) (2020). https://doi.org/10.1117/1.JMI.7.1.016502

  5. M.A. Khan et al., Lungs cancer classification from CT images: an integrated design of contrast based classical features fusion and selection. Pattern Recogn. Lett. 129, 77–85 (2020). https://doi.org/10.1016/j.patrec.2019.11.014

    Article  Google Scholar 

  6. S. Wang, R.M. Summers, Machine learning and radiology. Med. Image Anal. 16(5), 933–951 (2012). https://doi.org/10.1016/j.media.2012.02.005

    Article  Google Scholar 

  7. Gautham A, Bhateja V, Tiwari A, Satapathy SC (2018) An improved mammogram classification approach using back propagation neural network. Data Eng. Intell. Comput. 542, 283–291 (2018). https://doi.org/10.1007/978-981-10-3223-3

  8. V. Bhateja, M. Misra, S. Urooj, Non-linear polynomial filters for edge enhancement of mammogram lesions. Comput. Methods Programs Biomed. 129, 125–134 (2016). https://doi.org/10.1016/j.cmpb.2016.01.007

    Article  Google Scholar 

  9. V.P. Vianna, Study and development of a computer-aided diagnosis system for classification of chest X-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation 1–7 (2018)

    Google Scholar 

  10. H. Greenspan, B. Van Ginneken, R.M. Summers, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016). https://doi.org/10.1109/TMI.2016.2553401

    Article  Google Scholar 

  11. L. Pigou, S. Dieleman, P.J. Kindermans, B. Schrauwen, Sign language recognition using convolutional neural networks, in Lecture Notes in Computer Science. Lecture notes in artificial intelligence. Lecture notes in bioinformatics, vol. 8925 (2015), pp. 572–578. https://doi.org/10.1007/978-3-319-16178-5_40

  12. WHO/UNITAID, Tuberculosis Diagnostics Technology and Market Landscape (UNITAID Secr. World Health Organization, 2014)

    Google Scholar 

  13. P. Maduskar, M. Muyoyeta, H. Ayles, L. Hogeweg, L. Peters-Bax, B. Van Ginneken, Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers. Int. J. Tuberc. Lung Dis. 17(12), 1613–1620 (2013). https://doi.org/10.5588/ijtld.13.0325

    Article  Google Scholar 

  14. L. Hogeweg, C. Mol, P.A. De Jong, R. Dawson, H. Ayles, B. Van Ginneken, Fusion of local and global detection systems to detect tuberculosis in chest radiographs, in Lecture Notes in Computer Science. Lecture notes in artificial intelligence. Lecture notes in bioinformatics, vol. 6363, LNCS, No. Part 3 (2010), pp. 650–657. https://doi.org/10.1007/978-3-642-15711-0_81

  15. M. Muyoyeta et al., The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with Xpert MTB/RIF in Lusaka Zambia. PLoS ONE 9(4), 16–18 (2014). https://doi.org/10.1371/journal.pone.0093757

    Article  Google Scholar 

  16. M. Muyoyeta et al., Digital CXR with computer aided diagnosis versus symptom screen to define presumptive tuberculosis among household contacts and impact on tuberculosis diagnosis. BMC Infect. Dis. 17(1), 1–8 (2017). https://doi.org/10.1186/s12879-017-2388-7

    Article  Google Scholar 

  17. A. Steiner et al., Screening for pulmonary tuberculosis in a Tanzanian prison and computer-aided interpretation of chest X-rays. Public Health Action 5(4), 249–254 (2015). https://doi.org/10.5588/pha.15.0037

    Article  Google Scholar 

  18. R.H.H.M. Philipsen et al., Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs. Sci. Rep. 5(March), 1–8 (2015). https://doi.org/10.1038/srep12215

    Article  Google Scholar 

  19. J. Melendez et al., An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci. Rep. 6(October 2015), 1–8 (2016). https://doi.org/10.1038/srep25265

  20. J. Melendez, R.H.H.M. Philipsen, V. Sunkutu, N. Kapata, Automatic versus human reading of chest X-rays in the Zambia National Tuberculosis Prevalence Survey 21(April), 880–886 (2017)

    Google Scholar 

  21. K. Murphy et al., Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system (2019), pp. 1–11 [Online]. Available: https://arxiv.org/abs/1903.03349

  22. Our Improved CAD4TB Software is Now Reliable for Children | Delft Imaging. https://www.delft.care/cad4tb6/

  23. Qure.ai Chest X Ray Study. https://qure.ai/qxr/

  24. M. Nash et al., Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India. Sci. Rep. 10(1), 1–10 (2020). https://doi.org/10.1038/s41598-019-56589-3

    Article  Google Scholar 

  25. Qure.ai | qXR Becomes First AI-Based Chest X-ray Interpretation Tool to Receive CE Certification. https://qure.ai/news/2018/05/31/qXR-CE.html

  26. Lunit INSIGHT for Chest Radiography. https://insight.lunit.io/cxr/

  27. Z.Z. Qin et al., Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci. Rep. 1–10 (2019). https://doi.org/10.1038/s41598-019-51503-3

  28. TimBre | Docturnal. https://www.docturnal.com/products/timbre/

  29. M. Breuninger et al., Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-Saharan Africa. PLoS One 9(9) (2014). https://doi.org/10.1371/journal.pone.0106381

  30. M.T. Rahman et al., An evaluation of automated chest radiography reading software for tuberculosis screening among public- and private-sector patients. Eur. Respir. J. 49(5) (2017). https://doi.org/10.1183/13993003.02159-2016

  31. S. Mohammad, A. Zaidi, S.S. Habib, B. Van Ginneken, Evaluation of the diagnostic accuracy of computer-aided detection of tuberculosis on chest radiography among private sector patients in Pakistan. Sci. Rep. (July), 1–9 (2018). https://doi.org/10.1038/s41598-018-30810-1

  32. J. Melendez et al., Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening. Int. J. Tuberc. Lung Dis. 22(5), 567–571 (2018). https://doi.org/10.5588/ijtld.17.0492

    Article  Google Scholar 

  33. R.C. Koesoemadinata et al., Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus. Int. J. Tuberc. Lung Dis. 22(9), 1088–1094 (2018). https://doi.org/10.5588/ijtld.17.0827

    Article  Google Scholar 

  34. M.H. Id et al., A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest X-rays for pulmonary tuberculosis 1–19 (2019)

    Google Scholar 

  35. P. Putha et al., Can artificial intelligence reliably report chest X-rays? Radiologist validation of an algorithm trained on 2.3 million X-rays 1–13 (2018). [Online]. Available: https://arxiv.org/abs/1807.07455

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Puttagunta, M., Ravi, S. (2021). Deep Learning-Based Software Tools for Tuberculosis Detection in Chest X-Ray Images. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_21

Download citation

Publish with us

Policies and ethics