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Chest X-ray analysis empowered with deep learning: A systematic review
Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393235/ https://www.ncbi.nlm.nih.gov/pubmed/36034154 http://dx.doi.org/10.1016/j.asoc.2022.109319 |
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author | Meedeniya, Dulani Kumarasinghe, Hashara Kolonne, Shammi Fernando, Chamodi Díez, Isabel De la Torre Marques, Gonçalo |
author_facet | Meedeniya, Dulani Kumarasinghe, Hashara Kolonne, Shammi Fernando, Chamodi Díez, Isabel De la Torre Marques, Gonçalo |
author_sort | Meedeniya, Dulani |
collection | PubMed |
description | Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. |
format | Online Article Text |
id | pubmed-9393235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93932352022-08-22 Chest X-ray analysis empowered with deep learning: A systematic review Meedeniya, Dulani Kumarasinghe, Hashara Kolonne, Shammi Fernando, Chamodi Díez, Isabel De la Torre Marques, Gonçalo Appl Soft Comput Article Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. Elsevier B.V. 2022-09 2022-07-18 /pmc/articles/PMC9393235/ /pubmed/36034154 http://dx.doi.org/10.1016/j.asoc.2022.109319 Text en © 2022 Elsevier B.V. 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 Meedeniya, Dulani Kumarasinghe, Hashara Kolonne, Shammi Fernando, Chamodi Díez, Isabel De la Torre Marques, Gonçalo Chest X-ray analysis empowered with deep learning: A systematic review |
title | Chest X-ray analysis empowered with deep learning: A systematic review |
title_full | Chest X-ray analysis empowered with deep learning: A systematic review |
title_fullStr | Chest X-ray analysis empowered with deep learning: A systematic review |
title_full_unstemmed | Chest X-ray analysis empowered with deep learning: A systematic review |
title_short | Chest X-ray analysis empowered with deep learning: A systematic review |
title_sort | chest x-ray analysis empowered with deep learning: a systematic review |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393235/ https://www.ncbi.nlm.nih.gov/pubmed/36034154 http://dx.doi.org/10.1016/j.asoc.2022.109319 |
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