Open Access

ARTICLE

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

by Ha Manh Toan¹, Lam Thanh Hien², Ngo Duc Vinh³, Do Nang Toan^1,*

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. https://doi.org/10.32604/cmc.2023.033429

Received 16 June 2022; Accepted 28 September 2022; Issue published 28 December 2022

Abstract

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.

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Keywords

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