Open Access
ARTICLE
Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset
Nguyen Trong Vinh1, Lam Thanh Hien1, Ha Manh Toan2, Ngo Duc Vinh3, Do Nang Toan2,*
1
Faculty of Information Technology, Lac Hong University, Bien Hoa, Dong Nai, 76120, Vietnam
2
Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, 10072, Vietnam
3
Faculty of Information Technology, Hanoi University of Industry, Hanoi, 11915, Vietnam
* Corresponding Author: Do Nang Toan. Email: dntoan@ioit.ac.vn
(This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )
Intelligent Automation & Soft Computing https://doi.org/10.32604/iasc.2024.045297
Received 23 August 2023; Accepted 24 January 2024; Published online 02 April 2024
Abstract
Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly,
in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection
of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by
the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality
demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services,
such as an automatic tuberculosis diagnosis system. In our study, aiming to build that system, we were interested in
the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients. The chest X-ray image is
an important data type to diagnose tuberculosis, and it has also received a lot of attention from deep learning
researchers. This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset. In detail, we designed our custom convolutional
neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization
as a heat-map. To prove the performance of our network model, we conducted several experiments to compare
it to another study and also to evaluate it with the dataset of this research. To support the implementation, we
built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National
Lung Hospital. A large experiment dataset was also from this hospital, and most of this data was for training the
convolutional neural network model. The experiment results were evaluated regarding sensitivity, specificity, and
accuracy. We achieved high scores with a training accuracy score of 0.99, and the testing specificity and sensitivity
scores were over 0.9. Based on the X-ray image classification result, we visualize prediction results as heat-maps
and also analyze them in comparison with annotated symptoms of radiologists.
Keywords
Tuberculosis classification; Vietnamese chest X-ray; deep learning