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Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking
One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVI...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180385/ https://www.ncbi.nlm.nih.gov/pubmed/34764605 http://dx.doi.org/10.1007/s10489-021-02393-4 |
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author | Jangam, Ebenezer Barreto, Aaron Antonio Dias Annavarapu, Chandra Sekhara Rao |
author_facet | Jangam, Ebenezer Barreto, Aaron Antonio Dias Annavarapu, Chandra Sekhara Rao |
author_sort | Jangam, Ebenezer |
collection | PubMed |
description | One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset. |
format | Online Article Text |
id | pubmed-8180385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-81803852021-06-07 Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking Jangam, Ebenezer Barreto, Aaron Antonio Dias Annavarapu, Chandra Sekhara Rao Appl Intell (Dordr) Article One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset. Springer US 2021-06-07 2022 /pmc/articles/PMC8180385/ /pubmed/34764605 http://dx.doi.org/10.1007/s10489-021-02393-4 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 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 | Article Jangam, Ebenezer Barreto, Aaron Antonio Dias Annavarapu, Chandra Sekhara Rao Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title | Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title_full | Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title_fullStr | Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title_full_unstemmed | Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title_short | Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking |
title_sort | automatic detection of covid-19 from chest ct scan and chest x-rays images using deep learning, transfer learning and stacking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180385/ https://www.ncbi.nlm.nih.gov/pubmed/34764605 http://dx.doi.org/10.1007/s10489-021-02393-4 |
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