  For testing patients infected with COVID-19, along with RT-PCR testing, chest
radiology images are being used. For the detection of COVID-19 from radiology
images, many organizations are proposing the use of Deep Learning. University
of Waterloo and DarwinAI, have designed their own Deep Learning model
COVIDNet-CT to detect COVID-19 from infected chest CT images. Additionally,
they have introduced a CT image dataset COVIDx-CT, from CT images collected by
the China National Center for Bioinformation. COVIDx-CT contains 104,009 CT
image slices across 1,489 patient cases. After obtaining remarkable results on
the identification of COVID-19 from chest X-ray images by using the COGNEX
VisionPro Deep Learning Software 1.0 this time we test the performance of the
software on the identification of COVID-19 from CT images. COGNEX Deep Learning
Software: VisionPro Deep Learning, is a Deep Learning software that is used
across various domains ranging from factory automation to life sciences. In
this study, we train the classification model on 82,818 chest CT training and
validation images from the COVIDx-CT dataset in 3 classes - normal, pneumonia,
and COVID-19 and then test the results of the classification on the 21,191 test
images are compared with the results of COVIDNet-CT and various other state of
the art Deep Learning models from the open-source community. Also, we test how
reducing the number of images in the training set effects the results of the
software. Overall, VisionPro Deep Learning gives the best results with F-scores
over 99%, even as the number of images in the training set is reduced
significantly. This software is by no means a stand-alone solution in the
detection of COVID-19 but can aid radiologists and clinicians in achieving
faster and understandable diagnosis using the full potential of Deep Learning,
without the prerequisite of having to code in any programming language.
