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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Vydané v:Neural computing & applications Ročník 33; číslo 24; s. 16899 - 16919
Hlavní autori: Houssein, Essam H., Emam, Marwa M., Ali, Abdelmgeid A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.12.2021
Springer Nature B.V
Predmet:
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
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Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06273-3