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

TL;DR

This study evaluates the effectiveness of COGNEX VisionPro Deep Learning software in classifying COVID-19 from chest X-ray images, comparing its performance with open-source models like COVID-Net, and explores interpretability through lung segmentation.

Contribution

It demonstrates that COGNEX VisionPro Deep Learning achieves comparable or superior accuracy to open-source models in COVID-19 detection from X-rays, with an emphasis on interpretability via lung segmentation.

Findings

VisionPro achieves 94.0% F-score on entire images.

Segmentation improves F-score to 95.3%.

Performance is comparable or better than COVID-Net.

Abstract

The COVID-19 pandemic has been having a severe and catastrophic effect on humankind and is being considered the most crucial health calamity of the century. One of the best methods of detecting COVID-19 is from radiological images, namely X-rays and Computed Tomography or CT scan images. Many companies and educational organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, the University of Waterloo, along with Darwin AI, has designed its Deep Learning model COVID-Net and created a dataset called COVIDx, consisting of 13,975 images. In this study, COGNEXs Deep Learning Software-VisionPro Deep Learning is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state of the art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, firstly by selecting the entire image, that is, selecting the entire image as the Region of Interest-ROI, and secondly by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results-on the entire image as the ROI it achieves an overall F-score of 94.0 percent, and on the segmented lungs, it gets an F-score of 95.3 percent, which is at par or better than COVID-Net and other state of the art open-source Deep Learning models.