Skip to main content

Advertisement

Springer Nature Link
Log in

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

  • Review Article
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Tuberculosis is a serious threat to humankind. Every year millions of people are dying as it remains challenging to public health & care. The diversity of healthcare epidemiological settings has aggravated the situation. Third-world countries apply conventional methods to diagnose TB. They take a long time to give a result. Mainly blood, culture, sputum, and biopsies are examined. WHO has marked TB as a serious cause of death among ten other top life-threatening issues globally. The shortage of better healthcare services has worsened the situation in rural India. Recent advancements in medical sciences have proven significant success in controlling this contagious disease after being framed with artificial intelligence. There is a need for cost-effective screening and diagnosis at the initial stage of TB. To hold active TB cases demands new diagnostic approaches. The AI advancement during the last 10 years has been reviewed and analyzed in this paper.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Linh NN, Marks GB, Crawford ABH. Radiographic predictors of subsequent reactivation of tuberculosis. Int J Tuberc Lung Dis. 2007;11(10):1136–42.

    Google Scholar 

  2. Xu T, Cheng I, Long R, Mandal M. Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs. EURASIP J Image Video Process. 2013;1:3.

    Article  Google Scholar 

  3. Song A, Yang Y. Localization algorithm and implementation for focal of pulmonary tuberculosis chest image. In: 2010 international conference on machine vision and human-machine interface, IEEE; 2010. p. 361–4.

  4. Jaeger S, Karargyris A, Antani S. Detecting tuberculosis in radiographs using combined lung masks. IEEE. 2012;2012:4978–81.

    Google Scholar 

  5. Vajda S, Karargyris A, Jaeger S, Santosh KC, Candemir S, Xue Z, Thoma G. Feature selection for automatic tuberculosis screening in frontal chest radiographs. J Med Syst. 2018;42(8):146.

    Article  Google Scholar 

  6. Melendez J, Sánchez CI, Philipsen RH, Maduskar P, Dawson R, Theron G, Van Ginneken B. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci Rep. 2016;6:25265.

    Article  Google Scholar 

  7. Murphy K, Habib SS, Zaidi SMA. Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system. Sci Rep. 2020;10:5492.

    Article  Google Scholar 

  8. Hwang S, Kim HE, Jeong J, Kim HJ. A novel approach for tuberculosis screening based on deep convolutional neural networks. Med Imaging. 2016;9785:97852W-W97861.

    Google Scholar 

  9. Er O, Temurtas F, Tanrikulu AC. Tuberculosis disease diagnosis using artificial neural networks. J Med Syst. 2010;34(3):299–302.

    Article  Google Scholar 

  10. Elveren E, Yumusak N. Tuberculosis disease diagnosis using artificial neural network trained with a genetic algorithm. J Med Syst. 2011;35(3):329–32.

    Article  Google Scholar 

  11. Ansari A, Gupta NK, Ekata E. Adaptive neuro-fuzzy system for tuberculosis. In: Parallel distributed and grid computing (PDGC), 2012 2nd IEEE international conference on; 2012. pp. 568–73.

  12. Omisore MO, Samuel OW, Atajeromavwo EJ. A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis. Appl Comput Inform. 2015;13:27–37.

    Article  Google Scholar 

  13. Takagi T, Sugeno M. Derivation of fuzzy control rules from human operator’s control action. In: Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis; 1983. pp. 55–60.

  14. Er O, Yumusak N, Temurtas F. Diagnosis of chest diseases using artificial immune system. Expert Syst Appl. 2012;39(2):1862–8.

    Article  Google Scholar 

  15. Shahaboddin S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petkovic D, Gani A, Kiah MLM. Tuberculosis disease diagnosis using artificial immune recognition system. Int J Med Sci. 2014;11(5):508–14.

    Article  Google Scholar 

  16. Saybani MR, Shamshirband S, Hormozi SG, Wah TY, Aghabozorgi S, Pourhoseingholi MA, Olariu T. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system. Iran Red Crescent Med J. 2015;17:4.

    Article  Google Scholar 

  17. Saybani MR, Shamshirband S, Golzari S, Wah TY, Saeed A, Kiah MLM, Balas VE. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system. Med Biol Eng Comput. 2016;54(2–3):385–99.

    Article  Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;2012:1097–105.

    Google Scholar 

  19. Islam MT, Aowal MA, Minhaz AT, Ashraf K. Abnormality detection and localization in chest X-rays using deep convolutional neural networks. 2017. https://arxiv.org/abs/1705.09850.

  20. Alcantara MF, Cao Y, Liu C, Liu B, Brunette M, Zhang N, Sun T, Zhang P, Chen Q, Li Y, et al. Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor communities in Peru. Smart Health. 2017;1:66–76.

    Article  Google Scholar 

  21. Lopez-Garnier S, Sheen P, Zimic M. Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images. PLoS ONE. 2019;14(2):e0212094. https://doi.org/10.1371/journal.pone.0212094.

    Article  Google Scholar 

  22. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT, Mahbub ZB, Ayari MA, Chowdhury MEH. Reliable tuberculosis detection using Chest X-ray with deep learning segmentation and visualization. IEEE Access. 2020. https://doi.org/10.1109/ACCESS.2020.

    Article  Google Scholar 

  23. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.

    Article  Google Scholar 

  24. Xiong Y, Ba X, Hou A, Zhang K, Chen L, Li T. Automatic detection of mycobacterium tuberculosis using artificial intelligence. J Thorac Dis. 2018;10(3):1936–40. https://doi.org/10.21037/jtd.2018.01.91.

    Article  Google Scholar 

  25. Goni I, Ngene CU, Manga I, Nataala A, Calvin SJ. Intelligent system for diagnosing tuberculosis using adaptive neuro-fuzzy. Asian J Res Comput Sci. 2018;2(1):1–9.

    Article  Google Scholar 

  26. Pasa F, Golkov V, Pfeiffer F, Cremers D, Pfeiffer D. Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci Rep. 2019;9(1):1–9.

    Article  Google Scholar 

  27. Dıaz-Huerta JL, Tellez-Anguiano ADC, Fraga-Aguilar M, Gutierrez-Gnecchi JA, Arellano-Calderon S. Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis”. PLoS ONE. 2019;14(7):e0218861. https://doi.org/10.1371/journal.pone.0218861.

    Article  Google Scholar 

  28. Shih Y-J, Ayles H, Lönnroth K, Claassens M, Lin H-H. Development and validation of a prediction model for active tuberculosis case finding among HIV-negative/unknown populations. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-42372-x

  29. Sathitratanacheewin S, Sunanta P, Pongpirul K. Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon. 2020;6: e04614. https://doi.org/10.1016/j.heliyon.2020.e04614.

    Article  Google Scholar 

  30. Jaeger S, Karargyris A, Candemir S, Siegelman J, Folio L, Antani S, Thoma G. Automatic screening for tuberculosis in chest radiographs: a survey. Quant Imaging Med Surg. 2013;3(2):89.

  31. Qin ZZ, Sander MS, Rai B. 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. 2019;9:15000. https://doi.org/10.1038/s41598-019-51503-3.

    Article  Google Scholar 

  32. Murphy K, Habib SS, Zaidi SM, Khowaja S, Khan A, Melendez J, Scholten ET, Amad F, Schalekamp S, Verhagen M, Philipsen RH. Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep 2020;10:5492. https://doi.org/10.1038/s41598-020-62148-y.

  33. Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR. Application of semi-supervised deep learning to lung sound analysis. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2016. pp. 804–7. https://doi.org/10.1109/EMBC.2016.7590823.

  34. Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, Goo JM, Aum J, Yim JJ, Park CM. Deep learning-based automatic detection algorithm development and evaluation group, development and validation of a deep learning–based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin Infect Dis 2019;69(5):739–47. https://doi.org/10.1093/cid/ciy967.

  35. Dinesh Jackson Samuel R, Rajesh Kanna B. Tuberculosis (TB) detection system using deep neural networks. Neural Comput Appl. 2019;31:1533–45. https://doi.org/10.1007/s00521-018-3564-4.

  36. Khan MT, Kaushik AC, Ji L, Malik SI, Ali S, Wei DQ. Artificial neural networks for prediction of tuberculosis disease. Front Microbiol. 2019;10:395.

  37. Shakhmametova GR, Vakkazov NO, Zulkarneev RKH. Identification of pathological formations in the lungs based on machine learning methods. Adv Soc Sci Educ Human Res. 2020;2020:483.

    Google Scholar 

Download references

Acknowledgements

The research papers reviewed in this study analyses AI applicability in Health Care in treating Tuberculosis. The author shows his gratitude towards the Respiratory & Lungs Diseases Department—Hamdard Institute of Medical Sciences & Research- New Delhi for their valuable information and guidance. The author is grateful to Dr Vijay Kumar Garg, Associate Professor—Computer Science & Engineering—LPU- Punjab for supervising the work.

Author information

Authors and Affiliations

  1. School of Computer Science and Applications, Lovely Professional University, Punjab, India

    Abdul Karim Siddiqui

  2. School of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Vijay Kumar Garg

Authors
  1. Abdul Karim Siddiqui
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vijay Kumar Garg
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Abdul Karim Siddiqui.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Intelligent Systems” guest edited by Geetha Ganesan, Lalit Garg, Renu Dhir, Vijay Kumar and Manik Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Siddiqui, A.K., Garg, V.K. An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification. SN COMPUT. SCI. 3, 34 (2022). https://doi.org/10.1007/s42979-021-00890-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00890-4

Keywords