  Tuberculosis (TB) is still recognized as one of the leading causes of death
worldwide. Recent advances in deep learning (DL) have shown to enhance
radiologists' ability to interpret chest X-ray (CXR) images accurately and with
fewer errors, leading to a better diagnosis of this disease. However, little
work has been done to develop models capable of diagnosing TB that offer good
performance while being efficient, fast and computationally inexpensive. In
this work, we propose LightTBNet, a novel lightweight, fast and efficient deep
convolutional network specially customized to detect TB from CXR images. Using
a total of 800 frontal CXR images from two publicly available datasets, our
solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906,
0.907 and 0.961, respectively, on an independent test subset. The proposed
model demonstrates outstanding performance while delivering a rapid prediction,
with minimal computational and memory requirements, making it highly suitable
for deployment in handheld devices that can be used in low-resource areas with
high TB prevalence. Code publicly available at
https://github.com/dani-capellan/LightTBNet.
