Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of inter...

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Veröffentlicht in:	Neural computing & applications Jg. 33; H. 24; S. 16899 - 16919
Hauptverfasser:	Houssein, Essam H., Emam, Marwa M., Ali, Abdelmgeid A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.12.2021 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computed tomography Computer Science Coronaviruses COVID-19 Data Mining and Knowledge Discovery Evaluation Heuristic methods Image classification Image Processing and Computer Vision Image segmentation Machine learning Medical imaging Optimization Original Original Article Probability and Statistics in Computer Science Signal to noise ratio Trigonometric functions Viral diseases COVID-19 CT images Manta ray foraging optimization Otsu’s method Multilevel thresholding image segmentation Meta-heuristics algorithms
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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