  Tuberculosis is a deadly infectious disease prevalent around the world. Due
to the lack of proper technology in place, the early detection of this disease
is unattainable. Also, the available methods to detect Tuberculosis is not
up-to a commendable standards due to their dependency on unnecessary features,
this make such technology obsolete for a reliable health-care technology. In
this paper, I propose a deep-learning based system which diagnoses tuberculosis
based on the important features in Chest X-rays along with original chest
X-rays. Employing our system will accelerate the process of tuberculosis
diagnosis by overcoming the need to perform the time-consuming sputum-based
testing method (Diagnostic Microbiology). In contrast to the previous methods
\cite{kant2018towards, melendez2016automated}, our work utilizes the
state-of-the-art ResNet \cite{he2016deep} with proper data augmentation using
traditional robust features like Haar \cite{viola2005detecting,viola2001rapid}
and LBP \cite{ojala1994performance,ojala1996comparative}. I observed that such
a procedure enhances the rate of tuberculosis detection to a highly
satisfactory level. Our work uses the publicly available pulmonary chest X-ray
dataset to train our network \cite{jaeger2014two}. Nevertheless, the publicly
available dataset is very small and is inadequate to achieve the best accuracy.
To overcome this issue I have devised an intuitive feature based data
augmentation pipeline. Our approach shall help the deep neural network
\cite{lecun2015deep,he2016deep,krizhevsky2012imagenet} to focus its training on
tuberculosis affected regions making it more robust and accurate, when compared
to other conventional methods that use procedures like mirroring and rotation.
By using our simple yet powerful techniques, I observed a 10\% boost in
performance accuracy.
