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Deep learning-based comprehensive review on pulmonary tuberculosis

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Abstract

In areas with high tuberculosis (TB) prevalence, high mortality rate has significantly increased over the past few decades. Even though tuberculosis can be treated, areas with high disease burden continue to have insufficient screening tools, leading to diagnostic delays and incorrect diagnoses. As a result of these challenges, a computer-aided diagnostics (CAD) system has been developed that can automatically detect tuberculosis. There are few different methods that can be used to screen for tuberculosis; however, chest X-ray (CXR) is most commonly used and strongly suggested because it is so effective in identifying lung irregularities. Over past ten years, we have seen a meteoric rise in amount of research conducted into application of machine learning strategies to examination of chest X-ray images for screening regarding pulmonary abnormalities. Particularly, we have also noticed significant interest in testing for TB. This attentiveness has increased in tandem with phenomenal progress that has been made in deep learning (DL), which is predominately founded on convolutional neural networks (CNNs). Because of these advancements, significant research contributions have been made in field of DL techniques for TB screening by utilizing CXR images. The main focus of this paper is to emphasize favorable methods and data collection, as well as methodological contributions, identify data collections, and identify challenges.

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References

  1. Bansal T, Jindal N (2022) An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques. Neural Comput Appl 34:1–3. https://doi.org/10.1007/s00521-022-06929-8

    Article  Google Scholar 

  2. Abdallah YMY, Alqahtani T (2019) Research in medical imaging using image processing techniques. In: Medical imaging-principles and applications. IntechOpen. https://doi.org/10.5772/intechopen.84360

  3. Tuberculosis research in the Netherlands. KNCV—Tuberculosefonds. https://www.kncvtbc.org/uploaded/2015/10/TBC1538_Whitepaper_WEB.pdf

  4. Ayaz M, Shaukat F, Raja G (2021) Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors. Phys Eng Sci Med 44(1):183–194. https://doi.org/10.1007/s13246-020-00966-0

    Article  PubMed  PubMed Central  Google Scholar 

  5. Munadi K, Muchtar K, Maulina N, Pradhan B (2020) Image enhancement for tuberculosis detection using deep learning. IEEE Access 8:217897–217907. https://doi.org/10.1109/ACCESS.2020.3041867

    Article  Google Scholar 

  6. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks (PDF). Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  7. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, 2015, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  8. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition In: IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, United States, 2016, pp 770–778 https://doi.org/10.1109/CVPR.2016.90

  9. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 7132–7141. https://doi.org/10.1109/CVPR.2018.00745

  10. Priya MS, Kadhar Nawaz GM (2017) Multilevel image thresholding using OTSU’s algorithm in image segmentation. Int J Sci Eng 8(5):101–106

    Google Scholar 

  11. Maleki F, Ovens K, Najafian K, Forghani B, Reinhold C, Forghani R (2020) Overview of machine learning Part 1: fundamentals and classic approaches. Neuroimaging Clin N Am 30(4):17–32. https://doi.org/10.1016/j.nic.2020.08.007

    Article  Google Scholar 

  12. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):160. https://doi.org/10.1007/s42979-021-00592-x

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kolambage N, Goonasekara H, Hewapathirana R (2020) Design, development and implementation of a machine learning-based predictive modelling tool to accurately predict thalassemia carrier state using full blood count indices and haemoglobin variants

  14. Sahu M, Dash R (2021) A survey on deep learning: convolution neural network (CNN). In: Smart innovation, systems and technologies, pp 317–325. https://doi.org/10.1007/978-981-15-6202-0_32

  15. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):53. https://doi.org/10.1186/s40537-021-00444-8

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4):611–629. https://doi.org/10.1007/s13244-018-0639-9

    Article  PubMed  PubMed Central  Google Scholar 

  17. Gullu M, Yilmaz M, Yilmaz I (2011) Application of back propagation artificial neural network for modelling local GPS/levelling geoid undulations: a comparative study. FIG Working Week 2011:18–22

    Article  Google Scholar 

  18. Guo C, Pleiss G, Sun Y, Weinberger KQ (2017) On calibration of modern neural networks. In: Proceedings of the 34th international conference on machine learning. JMLR.org, 70, pp 1321–1330 (ICML2017)

  19. Priya E, Srinivasan S (2013) Automated decision support system for tuberculosis digital images using evolutionary learning machines. Eur J Biomed Inform 9(2):6. https://doi.org/10.24105/ejbi.2013.09.2.2

    Article  Google Scholar 

  20. Kalhori SRN, Zeng X (2013) Evaluation and comparison of different machine learning methods to predict outcome of tuberculosis treatment course. J Intell Learn Syst Appl 05(3):184–193. https://doi.org/10.4236/jilsa.2013.53020

    Article  Google Scholar 

  21. Zhang Y, Hong J (2023) Challenges of deep learning in cancers. Technol Cancer Res Treat 22:15330338231173496. https://doi.org/10.1177/15330338231173495

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32(4):582–596. https://doi.org/10.1007/s10278-019-00227-x

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zheng C, Liu J, Qiu G (2016) Tuberculosis bacteria detection based on Random Forest using fluorescent images. In: 9th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI), 2016. pp 553–558. https://doi.org/10.1109/CISP-BMEI.2016.7852772

  24. Nyein Naing WY, Htike ZZ (2014) Advances in automatic tuberculosis detection in chest X-ray images. Signal Image Process: Int J 5(6):41–53. https://doi.org/10.5121/sipij.2014.5604

    Article  Google Scholar 

  25. Shamshirband S, Hessam S, Javidnia H, Amiribesheli M, Vahdat S, Petković D, Gani A, Kiah ML (2014) Tuberculosis disease diagnosis using artificial immune recognition system. Int J Med Sci 11(5):508–514. https://doi.org/10.7150/ijms.8249

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lewinsohn DM, Leonard MK, LoBue PA, Cohn DL, Daley CL, Desmond JK et al (2016) "Official American Thoracic Society/Infectious Diseases Society of America/Centers for Disease Control and prevention clinical practice guidelines: diagnosis of tuberculosis in adults and children. Clin Infect Dis 64(2):e1–e33. https://doi.org/10.1093/cid/ciw694

    Article  PubMed  Google Scholar 

  27. Lopes UK, Valiati JF (2017) Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput Biol Med 89:135–143. https://doi.org/10.1016/j.compbiomed.2017.08.001

    Article  CAS  PubMed  Google Scholar 

  28. Jeyavathana B, Ramasamy B, Pandian A (2017) An efficient feature extraction method for tuberculosis detection using chest radiographs. Int J Appl Environ Sci 12:227–240

    Google Scholar 

  29. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582. https://doi.org/10.1148/radiol.2017162326

    Article  PubMed  Google Scholar 

  30. Udayakumar E et al (2017) TB screening using SVM and CBC techniques. Curr Pediatr Res 21:338–342

    Google Scholar 

  31. Pattnaik A et al (2019) Predicting tuberculosis. Related lung deformities from CT scan images using 3D CNN CLEF

  32. Norval, M, Wang, Z, Sun, Y (2019) Pulmonary tuberculosis detection using deep learning convolutional neural networks. In: Proceedings of the 3rd international conference on video and image processing (ICVIP 2019). Association for Computing Machinery, pp 47–51. https://doi.org/10.1145/3376067.3376068

  33. Díaz-Huerta JL, Téllez-Anguiano ADC, Fraga-Aguilar M, Gutiérrez-Gnecchi JA, Arellano-Calderón S (2019) Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis. PLoS ONE 14(7):e0218861. https://doi.org/10.1371/journal.pone.0218861

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sathitratanacheewin S, Sunanta P, Pongpirul K (2020) Deep learning for automated classification of tuberculosis-related chest X-ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon 6(8):e04614. https://doi.org/10.1016/j.heliyon.2020.e04614

    Article  PubMed  PubMed Central  Google Scholar 

  35. Vilca HD, Melgarejo LM, Uchamaco GR, Mariño FC (2020) Tuberculosis detection architecture with image processing using the SIFT and K-means algorithm. Computación y Sistemas 24(3):989–997. https://doi.org/10.13053/CyS-24-3-3120

    Article  Google Scholar 

  36. Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, Barrett R, Banu S, Creswell J (2021) Tuberculosis detection from chest X-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digital Health 3(9):e543–e554. https://doi.org/10.1016/S2589-7500(21)00116-3

    Article  CAS  PubMed  Google Scholar 

  37. Acharya V, Dhiman G, Prakasha K, Bahadur P, Choraria A, Sushobhitha M, Sowjanya J, Prabhu S, Chadaga K, Viriyasitavat W, Kautish S (2022) AI-assisted tuberculosis detection and classification from chest X-rays using a deep learning normalization-free network model. Comput Intell Neurosci. https://doi.org/10.1155/2022/2399428

    Article  PubMed  PubMed Central  Google Scholar 

  38. Singh M, Pujar GV, Kumar SA, Bhagyalalitha M, Akshatha HS, Abuhaija B, Alsoud AR, Abualigah L, Beeraka NM, Gandomi AH (2022) Evolution of machine learning in tuberculosis diagnosis: a review of deep learning-based medical applications. Electronics 11(17):2634. https://doi.org/10.3390/electronics11172634

    Article  Google Scholar 

  39. Goswami KK et al (2023) Deep learning classification of tuberculosis chest X-rays. Cureus 15:7e41583. https://doi.org/10.7759/cureus/41583

    Article  Google Scholar 

  40. Sharma V, Nillmani, Gupta SK, Shukla KK (2023) Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images. Intell Med 1:21–314. https://doi.org/10.1016/j.imed.2023.06.001

    Article  Google Scholar 

  41. Devasia J, Goswami H, Lakshminarayanan S, Rajaram M, Adithan S (2023) Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach. Sci Rep 13(1):887. https://doi.org/10.1038/s41598-023-28079-0

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  42. Kazemzadeh S, Yu J, Jamshy S, Pilgrim R, Nabulsi Z, Chen C, Beladia N, Lau C, McKinney SM, Hughes T, Kiraly AP, Kalidindi SR, Muyoyeta M, Malemela J, Shih T, Corrado GS, Peng L, Chou K, Chen PC, Prabhakara S (2023) Deep learning detection of active pulmonary tuberculosis at chest radiography matched the clinical performance of radiologists. Radiology 306(1):124–137. https://doi.org/10.1148/radiol.212213

    Article  PubMed  Google Scholar 

  43. Tasci E, Uluturk C, Ugur A (2021) A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection. Neural Comput Appl 33(22):15541–15555. https://doi.org/10.1007/s00521-021-06177-2

    Article  PubMed  PubMed Central  Google Scholar 

  44. Li X, Zhou Y, Du P, Lang G, Xu M, Wu W (2021) A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis. Appl Intell 51(6):4082–4093. https://doi.org/10.1007/s10489-020-02051-1

    Article  Google Scholar 

  45. Wang L, Ding W, Mo Y, Shi D, Zhang S, Zhong L, Wang K, Wang J, Huang C, Zhang S, Ye Z, Shen J, Xing Z (2021) Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework. Eur J Nuclear Med Mol Imaging 48(13):4293–4306. https://doi.org/10.1007/s00259-021-05432-x

    Article  Google Scholar 

  46. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  47. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  48. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. Computer vision–ECCV 2014. In: Proceedings, Part I 13: 13th european conference, Zurich, Switzerland, September 6–12, 2014. Springer, Berlin

  49. Xie S, Girshick R, Dollar P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: IEEE conference on computer vision and pattern recognition (CVPR) (2016), pp 5987–5995, https://doi.org/10.1109/CVPR.2017.634

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Authors and Affiliations

  1. Institute of Engineering and Technology, Chitkara University, Punjab, India

    Twinkle Bansal & Sheifali Gupta

  2. Faculty, ECED, TIET, Patiala, Punjab, India

    Neeru Jindal

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Bansal, T., Gupta, S. & Jindal, N. Deep learning-based comprehensive review on pulmonary tuberculosis. Neural Comput & Applic 36, 6513–6530 (2024). https://doi.org/10.1007/s00521-023-09381-4

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