Skip to main content
SpringerLink
Log in

Deep neural network model with Bayesian optimization for tuberculosis detection from X-Ray images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Tuberculosis is a chronic lung disease caused by bacterial infection, and more than 10 million people get this disease every year, especially in developing countries. Early diagnosis of tuberculosis is important for effective treatment. Thus, a new approach for diagnosing tuberculosis disease is proposed in this paper, which is based on the development of a deep neural network (DNN) model in which hyperparameters are determined using the Bayes optimization method. First, feature extraction was conducted using pre-trained deep learning models such as VGG16, EfficientNetB0, ResNet101, and DenseNet201 architectures in the proposed approach. Following that, four DNN models in which hyperparameters were selected using the Bayesian optimization method were developed utilizing these features extracted from pre-trained deep learning architectures. Finally, these DNN models were used to classify tuberculosis disease, and the classification performance of the developed models was compared. The results showed that the EfficientNetB0 model yields the best performance with 99.2857% accuracy, followed by VGG16 with an accuracy of 97.9286% and DenseNet201 with an accuracy of 97%. The ResNet101 model has the lowest accuracy with an accuracy of 95.6429%. Consequently, the best pre-trained model for extracting features from images as well as the most efficient and effective DNN structure for detecting tuberculosis disease has been revealed in this study.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Code availability

Deep learning based tuberculosis detection model is available online at: https://github.com/mrtucar/tuberculosis_classification.

References

  1. Ahsan M, Gomes R, Denton A (2019) Application of a convolutional neural network using transfer learning for tuberculosis detection. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp 427–433

  2. 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 44:183–194

    Article  Google Scholar 

  3. Bayesian Optimization library. https://scikit-optimize.github.io/stable/. Accessed 10 July 2021

  4. Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Neural networks: tricks of the trade. Springer, pp 437–478

  5. Brochu E, Cora VM, De Freitas N (2010) A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv Prepr arXiv10122599

  6. Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 8:713

    Article  Google Scholar 

  7. Chandra TB, Verma K, Singh BK et al (2020) Automatic detection of tuberculosis related abnormalities in chest X-ray images using hierarchical feature extraction scheme. Expert Syst Appl 158:113514

    Article  Google Scholar 

  8. Chauhan A, Chauhan D, Rout C (2014) Role of gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation. PLoS ONE 9:e112980

    Article  Google Scholar 

  9. Chollet F, others (2018) Keras: The python deep learning library. Astrophys Source Code Libr ascl–1806

  10. Fati SM, Senan EM, ElHakim N (2022) Deep and hybrid learning technique for early detection of tuberculosis based on X-ray images using feature fusion. Appl Sci 12:7092

    Article  Google Scholar 

  11. Furin J, Cox H, Pai M (2019) Tuberculosis. Lancet 393:1642–1656. https://doi.org/10.1016/S0140-6736(19)30308-3

    Article  Google Scholar 

  12. Global Tuberculosis Report (2020) https://www.who.int/publications/i/item/9789240013131. Accessed 12 June 2021

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016–Decem, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  14. Howell JD (2016) Early clinical use of the X-ray. Trans Am Clin Climatol Assoc 127:341

    Google Scholar 

  15. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proc – 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2017–Janua, pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243

  16. Hwang S, Kim H-E, Jeong J, Kim H-J (2016) A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Medical imaging 2016: computer-aided diagnosis, pp 97852W

  17. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284:574–582

    Article  Google Scholar 

  18. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  19. Lopes UK, Valiati JF (2017) Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput Biol Med 89:135–143

    Article  Google Scholar 

  20. Munadi K, Muchtar K, Maulina N, Pradhan B (2020) Image enhancement for tuberculosis detection using deep learning. IEEE Access 8:217897–217907

    Article  Google Scholar 

  21. Oloko-Oba M, Viriri S (2022) A systematic review of deep learning techniques for tuberculosis detection from chest radiograph. Front Med 9:830515

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Rahman T, Khandakar A, Kadir MA et al (2020) Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8:191586–191601

    Article  Google Scholar 

  24. Shahriari B, Swersky K, Wang Z et al (2015) Taking the human out of the loop: a review of bayesian optimization. Proc IEEE 104:148–175

    Article  Google Scholar 

  25. Silverman C (1949) An appraisal of the contribution of mass radiography in the discovery of pulmonary tuberculosis. Am Rev Tuberc 60:466–482

    Google Scholar 

  26. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv Prepr arXiv14091556

  27. Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. arXiv Prepr arXiv12062944

  28. 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 33:15541–15555

    Article  Google Scholar 

  29. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. 36th Int Conf Mach Learn ICML 2019 2019–June, pp 10691–10700

  30. Vajda S, Karargyris A, Jaeger S et al (2018) Feature selection for automatic tuberculosis screening in frontal chest radiographs. J Med Syst 42:1–11

    Article  Google Scholar 

  31. Wang Y (2021) Survey on deep multi-modal data analytics: collaboration, rivalry, and fusion. ACM Trans Multimed Comput Commun Appl 17:1–25

    Google Scholar 

  32. Wu L, Wang Y, Li X, Gao J (2018) Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE Trans Cybern 49:1791–1802

    Article  Google Scholar 

  33. Wu L, Hong R, Wang Y, Wang M (2019) Cross-entropy adversarial view adaptation for person re-identification. IEEE Trans Circuits Syst Video Technol 30:2081–2092

    Google Scholar 

  34. Wu J, Chen X-Y, Zhang H et al (2019) Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Technol 17:26–40

    Google Scholar 

  35. Wong A, Lee JRH, Rahmat-Khah H et al (2021) TB-Net: a tailored, self-attention deep convolutional neural network design for detection of tuberculosis cases from chest X-ray images. arXiv Prepr arXiv210403165

  36. Zhang J, Xie Y, Wu Q, Xia Y (2019) Medical image classification using synergic deep learning. Med Image Anal 54:10–19

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Information Systems, Faculty of Business and Management Sciences, İskenderun Technical University, Hatay, Turkey

    Murat Uçar

Authors
  1. Murat Uçar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Murat Uçar.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Additional information

Publisher’s note

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

Appendices

Appendix 1

Fig. 8

Fig. 8
figure 8

Hyperparameters and validation loss results of the DNN1(VGG16) model

Full size image

Appendix 2

Fig. 9

Fig. 9
figure 9

Hyperparameters and validation loss results of the DNN3(ResNet101) model

Full size image

Appendix 3

Fig. 10

Fig. 10
figure 10

Hyperparameters and validation loss results of the DNN4(DenseNet201) model

Full size image

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uçar, M. Deep neural network model with Bayesian optimization for tuberculosis detection from X-Ray images. Multimed Tools Appl 82, 36951–36972 (2023). https://doi.org/10.1007/s11042-023-15212-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15212-4

Keywords

Search

Navigation