  Tuberculosis (TB) is a contagious bacterial airborne disease, and is one of
the top 10 causes of death worldwide. According to the World Health
Organization (WHO), around 1.8 billion people are infected with TB and 1.6
million deaths were reported in 2018. More importantly,95% of cases and deaths
were from developing countries. Yet, TB is a completely curable disease through
early diagnosis. To achieve this goal one of the key requirements is efficient
utilization of existing diagnostic technologies, among which chest X-ray is the
first line of diagnostic tool used for screening for active TB. The presented
deep learning pipeline consists of three different state of the art deep
learning architectures, to generate, segment and classify lung X-rays. Apart
from this image preprocessing, image augmentation, genetic algorithm based
hyper parameter tuning and model ensembling were used to to improve the
diagnostic process. We were able to achieve classification accuracy of 97.1%
(Youden's index-0.941,sensitivity of 97.9% and specificity of 96.2%) which is a
considerable improvement compared to the existing work in the literature. In
our work, we present an highly accurate, automated TB screening system using
chest X-rays, which would be helpful especially for low income countries with
low access to qualified medical professionals.
