  People all over the globe are affected by pneumonia but deaths due to it are
highest in Sub-Saharan Asia and South Asia. In recent years, the overall
incidence and mortality rate of pneumonia regardless of the utilization of
effective vaccines and compelling antibiotics has escalated. Thus, pneumonia
remains a disease that needs spry prevention and treatment. The widespread
prevalence of pneumonia has caused the research community to come up with a
framework that helps detect, diagnose and analyze diseases accurately and
promptly. One of the major hurdles faced by the Artificial Intelligence (AI)
research community is the lack of publicly available datasets for chest
diseases, including pneumonia . Secondly, few of the available datasets are
highly imbalanced (normal examples are over sampled, while samples with ailment
are in severe minority) making the problem even more challenging. In this
article we present a novel framework for the detection of pneumonia. The
novelty of the proposed methodology lies in the tackling of class imbalance
problem. The Generative Adversarial Network (GAN), specifically a combination
of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein
GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia''
for augmentation, whereas Random Under-Sampling (RUS) was done on the majority
class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8
dataset, one of the biggest datasets, is used to validate the performance of
the proposed framework. The learning phase is completed using transfer learning
on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16.
Results obtained exceed state-of-the-art.
