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A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases

With the outbreak of COVID-19 and the increasing number of infections worldwide, there has been a noticeable deficiency in healthcare provided by medical professionals. To cope with this situation, computational methods can be used in different steps of COVID-19 handling. The first step is to accura...

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Detalles Bibliográficos
Autores principales: Aiadi, Oussama, Khaldi, Belal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212881/
https://www.ncbi.nlm.nih.gov/pubmed/35755317
http://dx.doi.org/10.1016/j.bspc.2022.103925
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author Aiadi, Oussama
Khaldi, Belal
author_facet Aiadi, Oussama
Khaldi, Belal
author_sort Aiadi, Oussama
collection PubMed
description With the outbreak of COVID-19 and the increasing number of infections worldwide, there has been a noticeable deficiency in healthcare provided by medical professionals. To cope with this situation, computational methods can be used in different steps of COVID-19 handling. The first step is to accurately and rapidly diagnose infected persons, because the time taken for the diagnosis is among the crucial factors to save human lives. This paper proposes a computationally fast network for the diagnosis of COVID-19 and pulmonary diseases, which can be used in telemedicine. The proposed network is called DLNet because it jointly encodes local binary patterns along with filter outputs of discrete cosine transform (DCT). The first layer in DLNet is the convolution layer in which the input image is convolved using DCT filters. Then, to avoid over-fitting, a binary hashing procedure is performed by fusing responses of different filters into a unique feature map. This map is used to generate block-wise histograms by binding local binary codes of the input image and the map values. We normalize these histograms to improve the robustness of the network against illumination changes. Experiments conducted on a public dataset demonstrate the rapidity and effectiveness of DLNet, where an average accuracy, sensitivity, and specificity of 98.86%, 98.06, and 99.24% have been achieved, respectively. Moreover, the proposed network has shown high tolerance to the missing parts in the medical image, which makes it suitable for the telemedicine scenario.
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spelling pubmed-92128812022-06-22 A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases Aiadi, Oussama Khaldi, Belal Biomed Signal Process Control Article With the outbreak of COVID-19 and the increasing number of infections worldwide, there has been a noticeable deficiency in healthcare provided by medical professionals. To cope with this situation, computational methods can be used in different steps of COVID-19 handling. The first step is to accurately and rapidly diagnose infected persons, because the time taken for the diagnosis is among the crucial factors to save human lives. This paper proposes a computationally fast network for the diagnosis of COVID-19 and pulmonary diseases, which can be used in telemedicine. The proposed network is called DLNet because it jointly encodes local binary patterns along with filter outputs of discrete cosine transform (DCT). The first layer in DLNet is the convolution layer in which the input image is convolved using DCT filters. Then, to avoid over-fitting, a binary hashing procedure is performed by fusing responses of different filters into a unique feature map. This map is used to generate block-wise histograms by binding local binary codes of the input image and the map values. We normalize these histograms to improve the robustness of the network against illumination changes. Experiments conducted on a public dataset demonstrate the rapidity and effectiveness of DLNet, where an average accuracy, sensitivity, and specificity of 98.86%, 98.06, and 99.24% have been achieved, respectively. Moreover, the proposed network has shown high tolerance to the missing parts in the medical image, which makes it suitable for the telemedicine scenario. Elsevier Ltd. 2022-09 2022-06-21 /pmc/articles/PMC9212881/ /pubmed/35755317 http://dx.doi.org/10.1016/j.bspc.2022.103925 Text en © 2022 Elsevier Ltd. All rights reserved. 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
Aiadi, Oussama
Khaldi, Belal
A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title_full A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title_fullStr A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title_full_unstemmed A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title_short A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases
title_sort fast lightweight network for the discrimination of covid-19 and pulmonary diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212881/
https://www.ncbi.nlm.nih.gov/pubmed/35755317
http://dx.doi.org/10.1016/j.bspc.2022.103925
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