Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays
Emmy Danny Ajik, Aminu Bashir Suleiman, Stephen Luka, Mukhtar Umar Shitu, Joseph Nda Ndabula
3 November 2025 · Vol. 1, Issue 3, pp. 82-88
DOI: 10.65150/EP-gjetr/V1E3/2025-01
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What is this article about?
Millions of reported cases and associated deaths highlight the annual global threat posed by Tuberculosis (TB). Added to this, limited diagnostic services, particularly in rural Nigeria, worsen the prevalence of TB in the country.
Article at a glance
- Published
- 3 November 2025
- Volume / issue / pages
- Vol. 1, Issue 3, pp. 82-88
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Millions of reported cases and associated deaths highlight the annual global threat posed by Tuberculosis (TB). Added to this, limited diagnostic services, particularly in rural Nigeria, worsen the prevalence of TB in the country. To address these challenges, this research explores the deployment of the deep learning model DenseNet121 to automate TB diagnosis from chest X-rays in low-resource settings such as Nigeria. The model aims to facilitate earlier TB detection in communities with inadequate access to diagnostic services. The absence of qualified TB radiologists in these communities further enhances the model’s potential. Based on analysis of a database comprising 4,200 chest X-ray images, the model achieved the following diagnostic metrics: 97.14% accuracy, 0.94 precision, 0.93 recall, and 0.90 F1 score. Such results provide sufficient evidence that the model will significantly improve the timely diagnosis and detection of TB cases. This illustrates the power of Artificial Intelligence tools in constraining and limited environments.
Keywords: Tuberculosis (TB), Chest X-rays, DenseNet121, Medical Imaging, Deep Learning
Cite this article
Emmy Danny Ajik, Aminu Bashir Suleiman, Stephen Luka, Mukhtar Umar Shitu, & Joseph Nda Ndabula (2025). Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays. Global Journal of Engineering and Technology Research, 1(3), 82-88. https://doi.org/10.65150/EP-gjetr/V1E3/2025-01
@article{Emmy2025,
title = {Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays},
author = {Emmy Danny Ajik and Aminu Bashir Suleiman and Stephen Luka and Mukhtar Umar Shitu and Joseph Nda Ndabula},
journal = {Global Journal of Engineering and Technology Research},
year = {2025},
volume = {1},
number = {3},
pages = {82-88},
doi = {10.65150/EP-gjetr/V1E3/2025-01},
url = {https://doi.org/10.65150/EP-gjetr/V1E3/2025-01}
}Questions about this article
Who wrote this article?
Emmy Danny Ajik, Aminu Bashir Suleiman, Stephen Luka, Mukhtar Umar Shitu, Joseph Nda Ndabula.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 3, pages 82-88, published by EP Journals Group on 3 November 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E3/2025-01 (https://doi.org/10.65150/EP-gjetr/V1E3/2025-01).
How do I cite this article?
APA: Emmy Danny Ajik, Aminu Bashir Suleiman, Stephen Luka, Mukhtar Umar Shitu, & Joseph Nda Ndabula (2025). Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays. Global Journal of Engineering and Technology Research, 1(3), 82-88. https://doi.org/10.65150/EP-gjetr/V1E3/2025-01
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