English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The COVID-19 pandemic has been having a severe and catastrophic effect onhumankind and is being considered the most crucial health calamity of thecentury. One of the best methods of detecting COVID-19 is from radiologicalimages, namely X-rays and Computed Tomography or CT scan images. Many companiesand educational organizations have come together during this crisis and createdvarious Deep Learning models for the effective diagnosis of COVID-19 from chestradiography images. For example, the University of Waterloo, along with DarwinAI, has designed its Deep Learning model COVID-Net and created a dataset calledCOVIDx, consisting of 13,975 images. In this study, COGNEXs Deep LearningSoftware-VisionPro Deep Learning is used to classify these Chest X-rays fromthe COVIDx dataset. The results are compared with the results of COVID-Net andvarious other state of the art Deep Learning models from the open-sourcecommunity. Deep Learning tools are often referred to as black boxes becausehumans cannot interpret how or why a model is classifying an image into aparticular class. This problem is addressed by testing VisionPro Deep Learningwith two settings, firstly by selecting the entire image, that is, selectingthe entire image as the Region of Interest-ROI, and secondly by segmenting thelungs in the first step, and then doing the classification step on thesegmented lungs only, instead of using the entire image. VisionPro DeepLearning results-on the entire image as the ROI it achieves an overall F-scoreof 94.0 percent, and on the segmented lungs, it gets an F-score of 95.3percent, which is at par or better than COVID-Net and other state of the artopen-source Deep Learning models.

Identification of images of COVID-19 from Chest X-rays using Deep  Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software with Open  Source Convolutional Neural Networks