Article
Version 1
Preserved in Portico This version is not peer-reviewed
Deep Learning for COVID-19 Recognition
Version 1
: Received: 28 May 2021 / Approved: 31 May 2021 / Online: 31 May 2021 (08:29:00 CEST)
How to cite: Zhang, Y.; Zhang, Y. Deep Learning for COVID-19 Recognition. Preprints 2021, 2021050711. https://doi.org/10.20944/preprints202105.0711.v1 Zhang, Y.; Zhang, Y. Deep Learning for COVID-19 Recognition. Preprints 2021, 2021050711. https://doi.org/10.20944/preprints202105.0711.v1
Abstract
Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.
Keywords
pneumonia; Resnet; residual; PEPX-Resnet; COVID-19
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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