Comparison of deep learning architectures for COVID-19 diagnosis using chest X-ray images

Abstract

The implementation of architectures based on artificial intelligence and deep learning to support COVID-19 diagnosis has great potential. However, especially in architectures designed at the beginning of the pandemic, they use different databases that do not contain a good amount of chest X-ray images of COVID-19 patients. The present work presents a comparison of three deep learning architectures (COVID-Net, CovXNet and DarkCovidNet) for COVID-19 diagnosis using chest Xray images. First, the architectures were implemented with the databases provided by the authors, to compare the results with those presented in the state of the art. Then, a new database with more than 9000 chest X-ray images of patients with COVID-19, pneumonia and healthy (3305 images for each class), was elaborated using databases from four different institutions around the world. Finally, the database was used to evaluate the original architectures, retrain them and, finally, evaluate the performance of the retrained architectures and compare results. It was identified that the architectures with the best performance and generalizability are DarkCovidNet and CovXNet with a support vector machine stacking algorithm, with an accuracy of 94.04% and 92.02% respectively, for the test data of the new database.