HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 24962012
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2020.106642
- Title of journal
- Applied Soft Computing
- Article number
- 106642
- First page
- 106642
- Volume
- 95
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Not compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper resolves problems in image segmentation and allows scientists to analyse thousands of X-ray images at once with more than 94% accuracy indices, and achieves better performance than several well-known algorithms. It provides a framework and recommendations for scientists to perform optimised and highly accurate COVID-19 analysis, and has been cited and developed by: Dutta and Banerjee, 2020 (10.36548/jscp.2020.4.001), Ekinci, 2020 (10.1109/ISMSIT50672.2020.9254597), Zhang, 2021 (10.1109/TII.2021.3051952), Tai, 2021 (10.1109/TII.2021.3052788), Song, 2021 (10.1109/TII.2021.3056686, including software developments https://github.com/Xuegang-S/AM-GCN), Tang, 2021 (10.1109/TII.2021.3057683), Castiglione, 2021 (10.1109/TII.2021.3057524). Scientists can fuse data effectively, compare X-ray images simultaneously, detect COVID-19 and predict likely outcomes accurately.
- Author contribution statement
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- Non-English
- No
- English abstract
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