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Diagnosis of Tuberculosis Using Medical Images by Convolutional Neural Networks

Document Type : Original Article

Authors

1 Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

2 Minimally Invasive Surgery Research Center, & Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

Abstract

Background: One of the best ways to reduce the spread of tuberculosis (TB) is to diagnose the disease using chest X-ray (CXR) images as a low-cost and affordable method. However, there are two problems: the lack of adequate radiologists and the possibility of misdiagnosis. This is why it is necessary to use an accessible and accurate diagnostic system. This research aimed to design an accurate and accessible automatic diagnosis system that can solve diagnosis problems using deep learning.
Methods: Six convolutional neural networks (CNNs), InceptionV3, ResNet50, DenseNet201, MnasNet, MobileNetV3, and EfficientNet-B4, were trained by transfer learning, the Adam optimizer, and 20 training epochs using the new, large, and accurate TBX11K dataset. The network was designed to categorize images into three groups: patients diagnosed with TB, patients exhibiting lung abnormalities unrelated to TB, and healthy individuals with no evidence of TB or other pulmonary anomalies within the lung imagery.
Results: In the testing step, the networks achieved very high performance. The EfficientNet-B4 network outperformed the other networks with a sensitivity of 97.1%, specificity of 99.9%, and accuracy of 99.5%. It also performed better than previous studies in TB diagnosis using CXR images by CNNs.
Conclusion: This research showed that with access to large high-quality datasets and standard training, it is possible to entrust the diagnosis of TB using medical images to computers and artificial neural networks with high confidence as they achieved accuracies higher than 99%.

Keywords

Main Subjects

References
  1. World Health Organization (WHO). Global Tuberculosis Report 2021. Geneva: WHO; 2021.
  2. Lönnroth K, Migliori GB, Abubakar I, D’Ambrosio L, de Vries G, Diel R, et al. Towards tuberculosis elimination: an action framework for low-incidence countries. Eur Respir J. 2015;45(4):928-52. doi: 10.1183/09031936.00214014.
  3. Guo R, Passi K, Jain CK. Tuberculosis diagnostics and localization in chest X-rays via deep learning models. Front Artif Intell. 2020;3:583427. doi: 10.3389/frai.2020.583427.
  4. Zaman K. Tuberculosis: a global health problem. J Health Popul Nutr. 2010;28(2):111-3. doi: 10.3329/jhpn.v28i2.4879.
  5. World Health Organization (WHO). Chest Radiography in Tuberculosis Detection: Summary of Current WHO Recommendations and Guidance on Programmatic Approaches. WHO; 2016.
  6. Gao J, Jiang Q, Zhou B, Chen D. Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview. Math Biosci Eng. 2019;16(6):6536-61. doi: 10.3934/mbe.2019326.
  7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. doi: 10.1038/nature14539.
  8. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al- Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8.
  9. Dhillon A, Verma GK. Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell. 2020;9(2):85-112. doi: 10.1007/ s13748-019-00203-0.
  10. O’Shea K, Nash R. An Introduction to Convolutional Neural Networks. ArXiv [Preprint]. 2015. Available from: https:// arxiv.org/abs/1511.08458.
  11. Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). Antalya, Turkey: IEEE; 2017. doi: 10.1109/ICEngTechnol.2017.8308186.
  12. Liu Y, Wu YH, Ban Y, Wang H, Cheng MM. Rethinking computer-aided tuberculosis diagnosis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2020.
  13. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611-29. doi: 10.1007/s13244- 018-0639-9.
  14. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016.
  15. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016.
  16. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2017.
  17. Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, et al. MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2019.
  18. Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, et al. Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE; 2019.
  19. Tan M, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning. PMLR; 2019.
  20. Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. ArXiv [Preprint]. 2014. Available from: https://arxiv.org/ abs/1412.6980.
  21. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging. 2016;35(5):1299-312. doi: 10.1109/ tmi.2016.2535302.
  22. Rosner BA. Fundamentals of Biostatistics. Cengage Learning; 2015.
  23. Liu Y. Rethinking Computer-Aided Tuberculosis Diagnosis. 2019. Available from: https://mmcheng.net/tb/.
  24. Hwang S, Kim HE, Jeong J, Kim HJ. A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Medical Imaging 2016: Computer-Aided Diagnosis. SPIE; 2016.
  25. Faruk O, Ahmed E, Ahmed S, Tabassum A, Tazin T, Bourouis S, et al. A novel and robust approach to detect tuberculosis using transfer learning. J Healthc Eng. 2021;2021:1002799. doi: 10.1155/2021/1002799.
  26. Hooda R, Sofat S, Kaur S, Mittal A, Meriaudeau F. Deep-learning: a potential method for tuberculosis detection using chest radiography. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). Kuching, Malaysia: IEEE; 2017. doi: 10.1109/icsipa.2017.8120663.
  27. Andika LA, Pratiwi H, Handajani SS. Convolutional neural network modeling for classification of pulmonary tuberculosis disease. J Phys Conf Ser. 2020;1490(1):012020. doi: 10.1088/1742-6596/1490/1/012020.
  28. Ghorakavi RS. TBNet: Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks. ArXiv [Preprint]. 2019. Available from: https://arxiv.org/abs/1902.08897.
  29. 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. doi: 10.1148/radiol.2017162326.
  30. Oloko-Oba M, Viriri S. Ensemble of EfficientNets for the diagnosis of tuberculosis. Comput Intell Neurosci. 2021;2021:9790894. doi: 10.1155/2021/9790894.
  31. Rohilla A, Hooda R, Mittal A. TB Detection in Chest Radiograph Using Deep Learning Architecture. ICETETSM-17. 2017. p. 136-47. Available from: http://data.conferenceworld. in/IETEAUGUST2017/15.pdf.
  32. Liu J, Huang Y. Comparison of different CNN models in tuberculosis detecting. KSII Transactions on Internet and Information Systems (TIIS). 2020;14(8):3519-33. doi: 10.3837/ tiis.2020.08.021.
  33. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, et al. Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access. 2020;8:191586-601. doi: 10.1109/access.2020.3031384.
  34. Oloko-Oba M, Viriri S. Diagnosing tuberculosis using deep convolutional neural network. In: El Moataz A, Mammass D, Mansouri A, Nouboud F, eds. Image and Signal Processing. Cham: Springer; 2020. p. 151-61 doi: 10.1007/978-3-030- 51935-3_16.
  35. Liu C, Cao Y, Alcantara M, Liu B, Brunette M, Peinado J, et al. TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network. In: 2017 IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE; 2017. doi: 10.1109/icip.2017.8296695.
  36. Hijazi MH, Hwa SK, Bade A, Yaakob R, Jeffree MS. Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images. IAES Int J Artif Intell. 2019;8(4):429-35. doi: 10.11591/ijai.v8.i4.pp429-435.
  37. Karnkawinpong T, Limpiyakorn Y. Classification of pulmonary tuberculosis lesion with convolutional neural networks. J Phys Conf Ser. 2019;1195(1):012007. doi: 10.1088/1742- 6596/1195/1/012007.
Journal of Kerman University of Medical Sciences
Volume 31, Issue 4
July and August 2024
Pages 180-186
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