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Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high...

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Autores principales: Rahman, Sejuti, Sarker, Sujan, Miraj, Md Abdullah Al, Nihal, Ragib Amin, Nadimul Haque, A. K. M., Noman, Abdullah Al
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921610/
https://www.ncbi.nlm.nih.gov/pubmed/33680209
http://dx.doi.org/10.1007/s12559-020-09779-5
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author Rahman, Sejuti
Sarker, Sujan
Miraj, Md Abdullah Al
Nihal, Ragib Amin
Nadimul Haque, A. K. M.
Noman, Abdullah Al
author_facet Rahman, Sejuti
Sarker, Sujan
Miraj, Md Abdullah Al
Nihal, Ragib Amin
Nadimul Haque, A. K. M.
Noman, Abdullah Al
author_sort Rahman, Sejuti
collection PubMed
description The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers’ interest. This survey marks a detailed inspection of the deep learning–based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.
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spelling pubmed-79216102021-03-02 Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis Rahman, Sejuti Sarker, Sujan Miraj, Md Abdullah Al Nihal, Ragib Amin Nadimul Haque, A. K. M. Noman, Abdullah Al Cognit Comput Article The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers’ interest. This survey marks a detailed inspection of the deep learning–based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field. Springer US 2021-03-02 /pmc/articles/PMC7921610/ /pubmed/33680209 http://dx.doi.org/10.1007/s12559-020-09779-5 Text en © 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
Rahman, Sejuti
Sarker, Sujan
Miraj, Md Abdullah Al
Nihal, Ragib Amin
Nadimul Haque, A. K. M.
Noman, Abdullah Al
Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title_full Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title_fullStr Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title_full_unstemmed Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title_short Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis
title_sort deep learning–driven automated detection of covid-19 from radiography images: a comparative analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921610/
https://www.ncbi.nlm.nih.gov/pubmed/33680209
http://dx.doi.org/10.1007/s12559-020-09779-5
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