Abstract
Tuberculosis, a contagious lung ailment, stands as a prominent global mortality factor. Its significant impact on public health in Nigeria necessitates comprehensive intervention strategies. Detecting, preventing, and treating this disease remains imperative. Chest X-ray (CXR) images hold a pivotal role among diagnostic tools. Recent strides in deep learning have notably improved medical image analysis. In this research, we harnessed publicly available and proprietary CXR image datasets to construct robust models. Leveraging pre-trained deep neural networks, we aimed to enhance tuberculosis detection. Impressively, our experimentation yielded remarkable outcomes. Notably, f1-scores of 98% and 86% were attained on the respective public and private datasets. These results underscore the potency of deep neural networks in effectively identifying tuberculosis from CXR images. The study emphasizes the promise of this technology in combating the disease's spread and impact.
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