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A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detectio...

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Autores principales: Kieu, Stefanus Tao Hwa, Bade, Abdullah, Hijazi, Mohd Hanafi Ahmad, Kolivand, Hoshang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321202/
https://www.ncbi.nlm.nih.gov/pubmed/34460528
http://dx.doi.org/10.3390/jimaging6120131
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author Kieu, Stefanus Tao Hwa
Bade, Abdullah
Hijazi, Mohd Hanafi Ahmad
Kolivand, Hoshang
author_facet Kieu, Stefanus Tao Hwa
Bade, Abdullah
Hijazi, Mohd Hanafi Ahmad
Kolivand, Hoshang
author_sort Kieu, Stefanus Tao Hwa
collection PubMed
description The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.
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spelling pubmed-83212022021-08-26 A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions Kieu, Stefanus Tao Hwa Bade, Abdullah Hijazi, Mohd Hanafi Ahmad Kolivand, Hoshang J Imaging Review The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications. MDPI 2020-12-01 /pmc/articles/PMC8321202/ /pubmed/34460528 http://dx.doi.org/10.3390/jimaging6120131 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Review
Kieu, Stefanus Tao Hwa
Bade, Abdullah
Hijazi, Mohd Hanafi Ahmad
Kolivand, Hoshang
A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title_full A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title_fullStr A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title_full_unstemmed A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title_short A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions
title_sort survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321202/
https://www.ncbi.nlm.nih.gov/pubmed/34460528
http://dx.doi.org/10.3390/jimaging6120131
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