  Since December of 2019, novel coronavirus disease COVID-19 has spread around
the world infecting millions of people and upending the global economy. One of
the driving reasons behind its high rate of infection is due to the
unreliability and lack of RT-PCR testing. At times the turnaround results span
as long as a couple of days, only to yield a roughly 70% sensitivity rate. As
an alternative, recent research has investigated the use of Computer Vision
with Convolutional Neural Networks (CNNs) for the classification of COVID-19
from CT scans. Due to an inherent lack of available COVID-19 CT data, these
research efforts have been forced to leverage the use of Transfer Learning.
This commonly employed Deep Learning technique has shown to improve model
performance on tasks with relatively small amounts of data, as long as the
Source feature space somewhat resembles the Target feature space.
Unfortunately, a lack of similarity is often encountered in the classification
of medical images as publicly available Source datasets usually lack the visual
features found in medical images. In this study, we propose the use of
Multi-Source Transfer Learning (MSTL) to improve upon traditional Transfer
Learning for the classification of COVID-19 from CT scans. With our
multi-source fine-tuning approach, our models outperformed baseline models
fine-tuned with ImageNet. We additionally, propose an unsupervised label
creation process, which enhances the performance of our Deep Residual Networks.
Our best performing model was able to achieve an accuracy of 0.893 and a Recall
score of 0.897, outperforming its baseline Recall score by 9.3%.
