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

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

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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.

Item Type: Article (Journal)
Uncontrolled Keywords: COVID-19, deep learning, convolutional neural network, colourmap
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Allied Health Sciences
Kulliyyah of Allied Health Sciences > Department of Diagnostic Imaging and Radiotherapy
Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science
Depositing User: Dr. Mohd Zulfaezal Che Azemin
Date Deposited: 26 Mar 2024 10:28
Last Modified: 26 Mar 2024 10:29
URI: http://irep.iium.edu.my/id/eprint/111539

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