In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.
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.
During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients.
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.
In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented.
Pre-training with a dataset of similar nature further improved accuracy to 98. 3% and specificity to 98. 6%.
Summary AI assistance improved radiologists’ performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT.
The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data.