https://arxiv.org/abs/2009.10474
Since December of 2019, novel Coronavirus COVID-19 has spread around the world infecting millions of people and upending the global economy. One of the main reasons for its high rate of infection is due to the unreliability and lack of RT-PCR testing. As an alternative, recent research has investigated the use of Convolutional Neural Networks for the classification of COVID-19 from CT scans. Because there is an inherent lack of available COVID-19 CT data, researchers are forced to leverage the use of Transfer Learning. Transfer Learning has shown to improve model performance on tasks that require relatively small amounts of data, as long as the Source feature space is not too different from the Target featurespace. This difference 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%.