COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.
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COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.Abstract:
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.read more
Citations
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Journal ArticleDOI
Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Journal ArticleDOI
Can AI Help in Screening Viral and COVID-19 Pneumonia?
Muhammad E. H. Chowdhury,Tawsifur Rahman,Amith Khandakar,Rashid Mazhar,Muhammad Abdul Kadir,Zaid Bin Mahbub,Khandakar Reajul Islam,Muhammad Salman Khan,Atif Iqbal,Nasser Al Emadi,Mamun Bin Ibne Reaz,Mohammad Tariqul Islam +11 more
TL;DR: The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy.
Journal ArticleDOI
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.
TL;DR: The proposed CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images achieves promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data.
Journal ArticleDOI
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
TL;DR: Experimental results show that the proposed patch-based convolutional neural network approach achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
Journal ArticleDOI
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.
TL;DR: In this article, a deep CNN, called Decompose, Transfer, and Compose (DeTraC), was used for the classification of COVID-19 chest X-ray images.
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