  Artificial intelligence (AI) is disrupting the medical field as advances in
modern technology allow common household computers to learn anatomical and
pathological features that distinguish between healthy and disease with the
accuracy of highly specialized, trained physicians. Computer vision AI
applications use medical imaging, such as lung chest X-Rays (LCXRs), to
facilitate diagnoses by providing second-opinions in addition to a physician's
or radiologist's interpretation. Considering the advent of the current
Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to
indirectly aid in infection containment, however generating a reliably labeled
image dataset for a novel disease is not an easy feat, nor is it of highest
priority when combating a global pandemic. Deep learning techniques such as
convolutional neural networks (CNNs) are able to select features that
distinguish between healthy and disease states for other lung pathologies; this
study aims to leverage that body of literature in order to apply image
transformations that would serve to balance the lack of COVID-19 LCXR data.
Furthermore, this study utilizes a simple CNN architecture for high-performance
multiclass LCXR classification at 94 percent accuracy.
