A Novel Implementation of Machine Learning for the Efficient,
Explainable Diagnosis of COVID-19 from Chest CT
Abstract
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.