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Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning

The Coronavirus epidemic first appeared in Wuhan-China on December, 31(st), 2019. This has put the world’s hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04(th), 2020, the World Health Organization reported more than 167515 confirmed cases in more th...

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Autores principales: Tabaa, Mohamed, Fahmani, Hamza, ouakifi, Mehdi El, Bensag, Hassna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954644/
https://www.ncbi.nlm.nih.gov/pubmed/33747260
http://dx.doi.org/10.1016/j.procs.2020.10.088
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author Tabaa, Mohamed
Fahmani, Hamza
ouakifi, Mehdi El
Bensag, Hassna
author_facet Tabaa, Mohamed
Fahmani, Hamza
ouakifi, Mehdi El
Bensag, Hassna
author_sort Tabaa, Mohamed
collection PubMed
description The Coronavirus epidemic first appeared in Wuhan-China on December, 31(st), 2019. This has put the world’s hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04(th), 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient’s personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information.
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spelling pubmed-79546442021-03-15 Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning Tabaa, Mohamed Fahmani, Hamza ouakifi, Mehdi El Bensag, Hassna Procedia Comput Sci Article The Coronavirus epidemic first appeared in Wuhan-China on December, 31(st), 2019. This has put the world’s hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04(th), 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient’s personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. The Author(s). Published by Elsevier B.V. 2020 2020-11-11 /pmc/articles/PMC7954644/ /pubmed/33747260 http://dx.doi.org/10.1016/j.procs.2020.10.088 Text en © 2020 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Tabaa, Mohamed
Fahmani, Hamza
ouakifi, Mehdi El
Bensag, Hassna
Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title_full Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title_fullStr Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title_full_unstemmed Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title_short Covid-19’s Rapid diagnosis Open platform based on X-Ray Imaging and Deep Learning
title_sort covid-19’s rapid diagnosis open platform based on x-ray imaging and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954644/
https://www.ncbi.nlm.nih.gov/pubmed/33747260
http://dx.doi.org/10.1016/j.procs.2020.10.088
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