Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is es...
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Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,imagesegmentation is a critical stage in image processing and *** achieve better imagesegmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevelthresholdimage *** imagesegmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the imagesegmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and imagesegmentation experiments compared with other meta-heuristic algorithms.
The multi-verse optimizer (MVO) algorithm has been applied to imagesegmentation, feature selection, engineering problems, and many other fields. MVO, like other metaheuristic algorithms, still has shortcomings, such ...
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The multi-verse optimizer (MVO) algorithm has been applied to imagesegmentation, feature selection, engineering problems, and many other fields. MVO, like other metaheuristic algorithms, still has shortcomings, such as poor convergence speed and quickly falling into local optimum. To address these concerns, this paper proposes CBQMVO, extending the original MVO algorithm with three strategies: covariance matrix adaptation strategy (CMAES), biogeography-based learning strategy (BLS), quasi-reflected and quasi-opposition strategy (QROS). CMAES can make the algorithm approach quickly the current local optimal solution and accelerate the convergence. BLS can enrich the population's diversity to discourage prematurely and assist the algorithm in jumping out of the local optimum. QROS can increase the probability of search particles falling near the optimal solution. A set of experiments were conducted to evaluate the performance of the CBQMVO. First, the original algorithm comparison experiment on IEEE CEC2014 includes strategy comparison, dimension comparison, exploration/exploitation balance, and population diversity experiments. Then, the advanced algorithm comparison experiment was carried out on IEEE CEC 2014. Furthermore, the champion algorithm comparison experiment was conducted on IEEE CEC2017 and IEEE CEC2020. A series of comparative experimental data demonstrate that CBQMVO has high performance, especially on some unimodal and complex competition functions. In addition, this paper also applied CBQMVO to implement Renyi's entropy multilevel threshold image segmentation based on the non-local mean 2D histogram (RMIS-2D) on breast cancer pathologic images. Compared with other metaheuristic algorithms and Kapur's entropy imagesegmentation, the proposed scheme in this paper has a better segmentation effect.
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