Cargando…

LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images

Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to...

Descripción completa

Detalles Bibliográficos
Autores principales: Ahmed, Noor, Tan, Xin, Ma, Lizhuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538131/
https://www.ncbi.nlm.nih.gov/pubmed/36246853
http://dx.doi.org/10.1049/ipr2.12637
_version_ 1784803317255241728
author Ahmed, Noor
Tan, Xin
Ma, Lizhuang
author_facet Ahmed, Noor
Tan, Xin
Ma, Lizhuang
author_sort Ahmed, Noor
collection PubMed
description Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future.
format Online
Article
Text
id pubmed-9538131
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-95381312022-10-11 LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images Ahmed, Noor Tan, Xin Ma, Lizhuang IET Image Process Original Research Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future. John Wiley and Sons Inc. 2022-09-24 /pmc/articles/PMC9538131/ /pubmed/36246853 http://dx.doi.org/10.1049/ipr2.12637 Text en © 2022 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Research
Ahmed, Noor
Tan, Xin
Ma, Lizhuang
LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title_full LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title_fullStr LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title_full_unstemmed LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title_short LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
title_sort lw‐covidnet: automatic covid‐19 lung infection detection from chest x‐ray images
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538131/
https://www.ncbi.nlm.nih.gov/pubmed/36246853
http://dx.doi.org/10.1049/ipr2.12637
work_keys_str_mv AT ahmednoor lwcovidnetautomaticcovid19lunginfectiondetectionfromchestxrayimages
AT tanxin lwcovidnetautomaticcovid19lunginfectiondetectionfromchestxrayimages
AT malizhuang lwcovidnetautomaticcovid19lunginfectiondetectionfromchestxrayimages