  Since the onset of the COVID-19 pandemic in 2020, millions of people have
succumbed to this deadly virus. Many attempts have been made to devise an
automated method of testing that could detect the virus. Various researchers
around the globe have proposed deep learning based methodologies to detect the
COVID-19 using Chest X-Rays. However, questions have been raised on the
presence of bias in the publicly available Chest X-Ray datasets which have been
used by the majority of the researchers. In this paper, we propose a 2 staged
methodology to address this topical issue. Two experiments have been conducted
as a part of stage 1 of the methodology to exhibit the presence of bias in the
datasets. Subsequently, an image segmentation, super-resolution and CNN based
pipeline along with different image augmentation techniques have been proposed
in stage 2 of the methodology to reduce the effect of bias. InceptionResNetV2
trained on Chest X-Ray images that were augmented with Histogram Equalization
followed by Gamma Correction when passed through the pipeline proposed in stage
2, yielded a top accuracy of 90.47% for 3-class (Normal, Pneumonia, and
COVID-19) classification task.
