Cargando…
A review of deep learning-based detection methods for COVID-19
COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have be...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Qatar University. Published by Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798789/ https://www.ncbi.nlm.nih.gov/pubmed/35180499 http://dx.doi.org/10.1016/j.compbiomed.2022.105233 |
_version_ | 1784641898572414976 |
---|---|
author | Subramanian, Nandhini Elharrouss, Omar Al-Maadeed, Somaya Chowdhury, Muhammed |
author_facet | Subramanian, Nandhini Elharrouss, Omar Al-Maadeed, Somaya Chowdhury, Muhammed |
author_sort | Subramanian, Nandhini |
collection | PubMed |
description | COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared. |
format | Online Article Text |
id | pubmed-8798789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Qatar University. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87987892022-01-31 A review of deep learning-based detection methods for COVID-19 Subramanian, Nandhini Elharrouss, Omar Al-Maadeed, Somaya Chowdhury, Muhammed Comput Biol Med Article COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared. Qatar University. Published by Elsevier Ltd. 2022-04 2022-01-29 /pmc/articles/PMC8798789/ /pubmed/35180499 http://dx.doi.org/10.1016/j.compbiomed.2022.105233 Text en © 2022 Qatar University 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 Subramanian, Nandhini Elharrouss, Omar Al-Maadeed, Somaya Chowdhury, Muhammed A review of deep learning-based detection methods for COVID-19 |
title | A review of deep learning-based detection methods for COVID-19 |
title_full | A review of deep learning-based detection methods for COVID-19 |
title_fullStr | A review of deep learning-based detection methods for COVID-19 |
title_full_unstemmed | A review of deep learning-based detection methods for COVID-19 |
title_short | A review of deep learning-based detection methods for COVID-19 |
title_sort | review of deep learning-based detection methods for covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798789/ https://www.ncbi.nlm.nih.gov/pubmed/35180499 http://dx.doi.org/10.1016/j.compbiomed.2022.105233 |
work_keys_str_mv | AT subramaniannandhini areviewofdeeplearningbaseddetectionmethodsforcovid19 AT elharroussomar areviewofdeeplearningbaseddetectionmethodsforcovid19 AT almaadeedsomaya areviewofdeeplearningbaseddetectionmethodsforcovid19 AT chowdhurymuhammed areviewofdeeplearningbaseddetectionmethodsforcovid19 AT subramaniannandhini reviewofdeeplearningbaseddetectionmethodsforcovid19 AT elharroussomar reviewofdeeplearningbaseddetectionmethodsforcovid19 AT almaadeedsomaya reviewofdeeplearningbaseddetectionmethodsforcovid19 AT chowdhurymuhammed reviewofdeeplearningbaseddetectionmethodsforcovid19 |