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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
by
Houssein, Essam H.
, Emam, Marwa M.
, Ali, Abdelmgeid A.
in
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
2021
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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
by
Houssein, Essam H.
, Emam, Marwa M.
, Ali, Abdelmgeid A.
in
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
2021
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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
by
Houssein, Essam H.
, Emam, Marwa M.
, Ali, Abdelmgeid A.
in
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
2021
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Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
Journal Article
Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
2021
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Overview
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 interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.
Publisher
Springer London,Springer Nature B.V
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