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COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening te...

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Autores principales: Al-Waisy, Alaa S., Al-Fahdawi, Shumoos, Mohammed, Mazin Abed, Abdulkareem, Karrar Hameed, Mostafa, Salama A., Maashi, Mashael S., Arif, Muhammad, Garcia-Zapirain, Begonya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7679792/
https://www.ncbi.nlm.nih.gov/pubmed/33250662
http://dx.doi.org/10.1007/s00500-020-05424-3
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author Al-Waisy, Alaa S.
Al-Fahdawi, Shumoos
Mohammed, Mazin Abed
Abdulkareem, Karrar Hameed
Mostafa, Salama A.
Maashi, Mashael S.
Arif, Muhammad
Garcia-Zapirain, Begonya
author_facet Al-Waisy, Alaa S.
Al-Fahdawi, Shumoos
Mohammed, Mazin Abed
Abdulkareem, Karrar Hameed
Mostafa, Salama A.
Maashi, Mashael S.
Arif, Muhammad
Garcia-Zapirain, Begonya
author_sort Al-Waisy, Alaa S.
collection PubMed
description The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
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spelling pubmed-76797922020-11-23 COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images Al-Waisy, Alaa S. Al-Fahdawi, Shumoos Mohammed, Mazin Abed Abdulkareem, Karrar Hameed Mostafa, Salama A. Maashi, Mashael S. Arif, Muhammad Garcia-Zapirain, Begonya Soft comput Focus The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result. Springer Berlin Heidelberg 2020-11-21 2023 /pmc/articles/PMC7679792/ /pubmed/33250662 http://dx.doi.org/10.1007/s00500-020-05424-3 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Focus
Al-Waisy, Alaa S.
Al-Fahdawi, Shumoos
Mohammed, Mazin Abed
Abdulkareem, Karrar Hameed
Mostafa, Salama A.
Maashi, Mashael S.
Arif, Muhammad
Garcia-Zapirain, Begonya
COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title_full COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title_fullStr COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title_full_unstemmed COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title_short COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images
title_sort covid-chexnet: hybrid deep learning framework for identifying covid-19 virus in chest x-rays images
topic Focus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7679792/
https://www.ncbi.nlm.nih.gov/pubmed/33250662
http://dx.doi.org/10.1007/s00500-020-05424-3
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