Cargando…
Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and imple...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Japan
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071474/ https://www.ncbi.nlm.nih.gov/pubmed/37229176 http://dx.doi.org/10.1007/s00354-023-00213-6 |
_version_ | 1785019210463707136 |
---|---|
author | Khattab, Rana Abdelmaksoud, Islam R. Abdelrazek, Samir |
author_facet | Khattab, Rana Abdelmaksoud, Islam R. Abdelrazek, Samir |
author_sort | Khattab, Rana |
collection | PubMed |
description | Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. |
format | Online Article Text |
id | pubmed-10071474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Japan |
record_format | MEDLINE/PubMed |
spelling | pubmed-100714742023-04-04 Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey Khattab, Rana Abdelmaksoud, Islam R. Abdelrazek, Samir New Gener Comput Article Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. Springer Japan 2023-04-04 2023 /pmc/articles/PMC10071474/ /pubmed/37229176 http://dx.doi.org/10.1007/s00354-023-00213-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Khattab, Rana Abdelmaksoud, Islam R. Abdelrazek, Samir Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title_full | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title_fullStr | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title_full_unstemmed | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title_short | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey |
title_sort | deep convolutional neural networks for detecting covid-19 using medical images: a survey |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071474/ https://www.ncbi.nlm.nih.gov/pubmed/37229176 http://dx.doi.org/10.1007/s00354-023-00213-6 |
work_keys_str_mv | AT khattabrana deepconvolutionalneuralnetworksfordetectingcovid19usingmedicalimagesasurvey AT abdelmaksoudislamr deepconvolutionalneuralnetworksfordetectingcovid19usingmedicalimagesasurvey AT abdelrazeksamir deepconvolutionalneuralnetworksfordetectingcovid19usingmedicalimagesasurvey |