Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public.
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training.
Pre-training with a dataset of similar nature further improved accuracy to 98. 3% and specificity to 98. 6%.
In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19.
At the next stage, we propose a modified version of ResNet50V2 that is enhanced by a feature pyramid network for classifying the selected CT images into COVID-19 or normal.