Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays
DOI:
https://doi.org/10.65150/EP-gjetr/V1E3/2025-01Keywords:
Tuberculosis (TB), Chest X-rays, DenseNet121, Medical Imaging, Deep LearningAbstract
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.
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Copyright (c) 2025 Emmy Danny Ajik, Aminu Bashir Suleiman, Stephen Luka, Mukhtar Umar Shitu, Joseph Nda Ndabula (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
