  Tuberculosis is an infectious disease that is leading to the death of
millions of people across the world. The mortality rate of this disease is high
in patients suffering from immuno-compromised disorders. The early diagnosis of
this disease can save lives and can avoid further complications. But the
diagnosis of TB is a very complex task. The standard diagnostic tests still
rely on traditional procedures developed in the last century. These procedures
are slow and expensive. So this paper presents an automatic approach for the
diagnosis of TB from posteroanterior chest x-rays. This is a two-step approach,
where in the first step the lung regions are segmented from the chest x-rays
using the graph cut method, and then in the second step the transfer learning
of VGG16 combined with Bi-directional LSTM is used for extracting high-level
discriminative features from the segmented lung regions and then classification
is performed using a fully connected layer. The proposed model is evaluated
using data from two publicly available databases namely Montgomery Country set
and Schezien set. The proposed model achieved accuracy and sensitivity of
97.76%, 97.01% and 96.42%, 94.11% on Schezien and Montgomery county datasets.
This model enhanced the diagnostic accuracy of TB by 0.7% and 11.68% on
Schezien and Montgomery county datasets.
