COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep
Learning
- URL: http://arxiv.org/abs/2006.13807v2
- Date: Wed, 29 Jul 2020 01:53:46 GMT
- Title: COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep
Learning
- Authors: Arman Haghanifar, Mahdiyar Molahasani Majdabadi, Younhee Choi, S.
Deivalakshmi, Seokbum Ko
- Abstract summary: In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia.
Research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset.
- Score: 6.098524160574895
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One of the primary clinical observations for screening the infectious by the
novel coronavirus is capturing a chest x-ray image. In most of the patients, a
chest x-ray contains abnormalities, such as consolidation, which are the
results of COVID-19 viral pneumonia. In this study, research is conducted on
efficiently detecting imaging features of this type of pneumonia using deep
convolutional neural networks in a large dataset. It is demonstrated that
simple models, alongside the majority of pretrained networks in the literature,
focus on irrelevant features for decision-making. In this paper, numerous chest
x-ray images from various sources are collected, and the largest publicly
accessible dataset is prepared. Finally, using the transfer learning paradigm,
the well-known CheXNet model is utilized for developing COVID-CXNet. This
powerful model is capable of detecting the novel coronavirus pneumonia based on
relevant and meaningful features with precise localization. COVID-CXNet is a
step towards a fully automated and robust COVID-19 detection system.
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