  Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains
a worldwide dilemma, continuously developing new variants such as Delta and the
recent Omicron. The current standard for testing is through polymerase chain
reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to
many people. X-rays on the other hand have been readily used since the early
20th century and are relatively cheaper, quicker to obtain, and typically
covered by health insurance. With a careful selection of model,
hyperparameters, and augmentations, we show that it is possible to develop
models with 83% accuracy in binary classification and 64% in multi-class for
detecting COVID-19 infections from chest x-rays.
