  In a worldwide health crisis as exigent as COVID-19, there has become a
pressing need for rapid, reliable diagnostics. Currently, popular testing
methods such as reverse transcription polymerase chain reaction (RT-PCR) can
have high false negative rates. Consequently, COVID-19 patients are not
accurately identified nor treated quickly enough to prevent transmission of the
virus. However, the recent rise of medical CT data has presented promising
avenues, since CT manifestations contain key characteristics indicative of
COVID-19. This study aimed to take a novel approach in the machine
learning-based detection of COVID-19 from chest CT scans. First, the dataset
utilized in this study was derived from three major sources, comprising a total
of 17,698 chest CT slices across 923 patient cases. Image preprocessing
algorithms were then developed to reduce noise by excluding irrelevant
features. Transfer learning was also implemented with the EfficientNetB7
pre-trained model to provide a backbone architecture and save computational
resources. Lastly, several explainability techniques were leveraged to
qualitatively validate model performance by localizing infected regions and
highlighting fine-grained pixel details. The proposed model attained an overall
accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed
that the model correctly distinguished between relevant, critical features
pertaining to COVID-19 chest CT images and normal controls. Deep learning
frameworks provide efficient, human-interpretable COVID-19 diagnostics that
could complement radiologist decisions or serve as an alternative screening
tool. Future endeavors may provide insight into infection severity, patient
risk stratification, and prognosis.
