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Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Ha Manh Toan1, Lam Thanh Hien2, Ngo Duc Vinh3, Do Nang Toan1,*

1 Institute of Information Technology–Vietnam Academy of Science and Technology, Hanoi, 10072, Vietnam
2 Lac Hong University, Bien Hoa, Dong Nai, 76120, Vietnam
3 Hanoi University of Industry, Hanoi, 11915, Vietnam

* Corresponding Author: Do Nang Toan. Email:

Computers, Materials & Continua 2023, 74(3), 5001-5016.


Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so early and simple detection of tuberculosis has been more and more important. In our study, we focused on tuberculosis diagnosis by using the chest X-ray image, the essential input for the radiologist’s profession, and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images. We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types. We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models. Our experiments were carried out by applying three different architectures, Alexnet, Resnet, and Densenet, on international, Vietnamese, and combined X-ray image datasets. After training, all models were verified on a pure Vietnamese X-rays set. The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC (Area under the Receiver Operating Characteristic Curve), sensitivity, specificity, and accuracy. In the best strategy, most of the scores were more than 0.93, and all AUCs were more than 0.98.


Cite This Article

H. M. Toan, L. Thanh Hien, N. D. Vinh and D. N. Toan, "Detecting tuberculosis from vietnamese x-ray imaging using transfer learning approach," Computers, Materials & Continua, vol. 74, no.3, pp. 5001–5016, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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