About: Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images

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About: Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images
Creator	Nguyen, Phuong Duong, Linh Flammini, Michele Iovino, Ludovico
source	MedRxiv
abstract	Abstract Background: The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treat- ments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diag- nosing this disease. Objective: This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques. Methods: We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classifica- tion engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively. Results: The results are promising: by all the experimental configurations considered in the evaluation,our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%. Conclusions: As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.
has issue date	2020-08-14(xsd:dateTime)
bibo:doi	10.1101/2020.08.13.20173997
has license	medrxiv
sha1sum (hex)	cf02eda4abb178855765e26cc753a82ed247968e
schema:url	https://doi.org/10.1101/2020.08.13.20173997
resource representing a document's title	Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images
resource representing a document's body	covid:cf02eda4abb178855765e26cc753a82ed247968e#body_text
is schema:about of	named entity 'mortality' named entity 'Objective' named entity 'testing' named entity 'experimental' named entity 'images' »more»

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