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Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap

Background: With the emergence of the SARS-CoV-2 virus late in 2019, the world’s healthcare system has been severely affected by the COVID-19 pandemic, necessitating the need for quick and effective actions to reduce its extensive effects. Chest X-ray (CXR) imaging is critical for accurate assessmen...

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Main Authors: Che Daud, Mohd. Zamzuri, Ahmad Zaiki, Farah Wahida, Che Azemin, Mohd. Zulfaezal
Format: Article
Language:English
Published: Institute for Health Management, Ministry of Health Malaysia 2023
Subjects:
R Medicine (General)
T Technology (General)
Online Access:http://irep.iium.edu.my/111539/1/111539_Deep%20learning-based%20analysis.pdf
http://irep.iium.edu.my/111539/
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spelling my.iium.irep.1115392024-03-26T02:29:17Z http://irep.iium.edu.my/111539/ Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap Che Daud, Mohd. Zamzuri Ahmad Zaiki, Farah Wahida Che Azemin, Mohd. Zulfaezal R Medicine (General) T Technology (General) Background: With the emergence of the SARS-CoV-2 virus late in 2019, the world’s healthcare system has been severely affected by the COVID-19 pandemic, necessitating the need for quick and effective actions to reduce its extensive effects. Chest X-ray (CXR) imaging is critical for accurate assessment, displaying intricate lung structural abnormalities, including ground-glass opacities, consolidation, and bilateral infiltrates in COVID-19 patients. The objective of this study was to examine the comparison between grayscale and 16 colourmap images in terms of their efficacy in COVID-19 detection when used with the DarkNet-53 deep learning architecture. Methodology: We conducted an experiment with a dataset of 9,665 CXRs, consisting of 7,134 normal images and 2,531 COVID-19 images, in order to train deep learning architectures. An additional dataset of 4,143 CXRs, with 3,058 normal and 1,085 COVID-19 images, was used for independent testing. The images underwent pre-processing and were split into grayscale and 16 colourmap images for individual examination. The COVID-19 detection task was fine-tuned on DarkNet-53, a deep learning architecture, with standard data augmentation techniques applied to grayscale and 16 colourmap images. Results: The DarkNet-53 deep learning architecture demonstrated verifying results based on the X-ray image utilised. The bone colourmap achieved the highest accuracy (0.985) and sensitivity (0.952) scores, while the grayscale, pink, and summer colourmaps demonstrated the greatest specificity (0.998). Conclusion: Our study highlights the importance of choosing the right type of X-ray image in association with deep learning architecture for CXR COVID-19 detection. These outcomes have important consequences for automating and upgrading CXR analysis, aiding in the exact detection of COVID-19 and respiratory health issues, and eventually benefiting patient care and outcomes. Institute for Health Management, Ministry of Health Malaysia 2023-12-29 Article PeerReviewed application/pdf en http://irep.iium.edu.my/111539/1/111539_Deep%20learning-based%20analysis.pdf Che Daud, Mohd. Zamzuri and Ahmad Zaiki, Farah Wahida and Che Azemin, Mohd. Zulfaezal (2023) Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap. Journal of Health Management, 20 (2). pp. 50-57. E-ISSN 2948-5126
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic R Medicine (General)
T Technology (General)
spellingShingle R Medicine (General)
T Technology (General)
Che Daud, Mohd. Zamzuri
Ahmad Zaiki, Farah Wahida
Che Azemin, Mohd. Zulfaezal
Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
description Background: With the emergence of the SARS-CoV-2 virus late in 2019, the world’s healthcare system has been severely affected by the COVID-19 pandemic, necessitating the need for quick and effective actions to reduce its extensive effects. Chest X-ray (CXR) imaging is critical for accurate assessment, displaying intricate lung structural abnormalities, including ground-glass opacities, consolidation, and bilateral infiltrates in COVID-19 patients. The objective of this study was to examine the comparison between grayscale and 16 colourmap images in terms of their efficacy in COVID-19 detection when used with the DarkNet-53 deep learning architecture. Methodology: We conducted an experiment with a dataset of 9,665 CXRs, consisting of 7,134 normal images and 2,531 COVID-19 images, in order to train deep learning architectures. An additional dataset of 4,143 CXRs, with 3,058 normal and 1,085 COVID-19 images, was used for independent testing. The images underwent pre-processing and were split into grayscale and 16 colourmap images for individual examination. The COVID-19 detection task was fine-tuned on DarkNet-53, a deep learning architecture, with standard data augmentation techniques applied to grayscale and 16 colourmap images. Results: The DarkNet-53 deep learning architecture demonstrated verifying results based on the X-ray image utilised. The bone colourmap achieved the highest accuracy (0.985) and sensitivity (0.952) scores, while the grayscale, pink, and summer colourmaps demonstrated the greatest specificity (0.998). Conclusion: Our study highlights the importance of choosing the right type of X-ray image in association with deep learning architecture for CXR COVID-19 detection. These outcomes have important consequences for automating and upgrading CXR analysis, aiding in the exact detection of COVID-19 and respiratory health issues, and eventually benefiting patient care and outcomes.
format Article
author Che Daud, Mohd. Zamzuri
Ahmad Zaiki, Farah Wahida
Che Azemin, Mohd. Zulfaezal
author_facet Che Daud, Mohd. Zamzuri
Ahmad Zaiki, Farah Wahida
Che Azemin, Mohd. Zulfaezal
author_sort Che Daud, Mohd. Zamzuri
title Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
title_short Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
title_full Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
title_fullStr Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
title_full_unstemmed Deep learning-based analysis of COVID-19 x-ray images: a comparative study of colourmap
title_sort deep learning-based analysis of covid-19 x-ray images: a comparative study of colourmap
publisher Institute for Health Management, Ministry of Health Malaysia
publishDate 2023
url http://irep.iium.edu.my/111539/1/111539_Deep%20learning-based%20analysis.pdf
http://irep.iium.edu.my/111539/
_version_ 1794632355619536896
score 13.211869

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