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Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation

The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this...

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Autores principales: Prakash, N.B., Murugappan, M., Hemalakshmi, G.R., Jayalakshmi, M., Mahmud, Mufti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364837/
https://www.ncbi.nlm.nih.gov/pubmed/34422549
http://dx.doi.org/10.1016/j.scs.2021.103252
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author Prakash, N.B.
Murugappan, M.
Hemalakshmi, G.R.
Jayalakshmi, M.
Mahmud, Mufti
author_facet Prakash, N.B.
Murugappan, M.
Hemalakshmi, G.R.
Jayalakshmi, M.
Mahmud, Mufti
author_sort Prakash, N.B.
collection PubMed
description The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.
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spelling pubmed-83648372021-08-16 Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation Prakash, N.B. Murugappan, M. Hemalakshmi, G.R. Jayalakshmi, M. Mahmud, Mufti Sustain Cities Soc Article The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen. Elsevier Ltd. 2021-12 2021-08-16 /pmc/articles/PMC8364837/ /pubmed/34422549 http://dx.doi.org/10.1016/j.scs.2021.103252 Text en © 2021 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
Prakash, N.B.
Murugappan, M.
Hemalakshmi, G.R.
Jayalakshmi, M.
Mahmud, Mufti
Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title_full Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title_fullStr Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title_full_unstemmed Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title_short Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
title_sort deep transfer learning for covid-19 detection and infection localization with superpixel based segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364837/
https://www.ncbi.nlm.nih.gov/pubmed/34422549
http://dx.doi.org/10.1016/j.scs.2021.103252
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