  We present a deep-learning based computing framework for fast-and-accurate CT
(DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to
improve the testing speed and accuracy of COVID-19 (plus its variants) via a
DL-based approach for CT image enhancement and classification. The image
enhancement network is adapted from DDnet, short for DenseNet and Deconvolution
based network. To demonstrate its speed and accuracy, we evaluated DL-FACT
across several sources of COVID-19 CT images. Our results show that DL-FACT can
significantly shorten the turnaround time from days to minutes and improve the
COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool
for medical professionals in diagnosing and monitoring COVID-19.
