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A stacked ensemble for the detection of COVID-19 with high recall and accuracy
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing di...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241584/ https://www.ncbi.nlm.nih.gov/pubmed/34247135 http://dx.doi.org/10.1016/j.compbiomed.2021.104608 |
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author | Jangam, Ebenezer Annavarapu, Chandra Sekhara Rao |
author_facet | Jangam, Ebenezer Annavarapu, Chandra Sekhara Rao |
author_sort | Jangam, Ebenezer |
collection | PubMed |
description | The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models: the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset. |
format | Online Article Text |
id | pubmed-8241584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82415842021-07-01 A stacked ensemble for the detection of COVID-19 with high recall and accuracy Jangam, Ebenezer Annavarapu, Chandra Sekhara Rao Comput Biol Med Article The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models: the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset. Elsevier Ltd. 2021-08 2021-06-30 /pmc/articles/PMC8241584/ /pubmed/34247135 http://dx.doi.org/10.1016/j.compbiomed.2021.104608 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 Jangam, Ebenezer Annavarapu, Chandra Sekhara Rao A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title | A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title_full | A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title_fullStr | A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title_full_unstemmed | A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title_short | A stacked ensemble for the detection of COVID-19 with high recall and accuracy |
title_sort | stacked ensemble for the detection of covid-19 with high recall and accuracy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241584/ https://www.ncbi.nlm.nih.gov/pubmed/34247135 http://dx.doi.org/10.1016/j.compbiomed.2021.104608 |
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