2023
DOI: 10.1016/j.bspc.2022.104373
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Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution

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Cited by 40 publications
(12 citation statements)
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“…A DE algorithm with additional strategies was proposed by Brest et al [46], and the test set assessment results revealed that the algorithm outperformed previous algorithms. Yang et al [47] proposed an enhanced DE based on elite gene mutation, and Wang [48] The improved variation strategy used in this study is as follows: the perturbation scale parameter ui in the variational strategy is larger, the individual vector is randomly selected, and the algorithm tends to perturb the entire population. This accelerates convergence and improves the global optimization-seeking ability of the algorithm.…”
Section: Optimization Of Variation Strategymentioning
confidence: 99%
“…A DE algorithm with additional strategies was proposed by Brest et al [46], and the test set assessment results revealed that the algorithm outperformed previous algorithms. Yang et al [47] proposed an enhanced DE based on elite gene mutation, and Wang [48] The improved variation strategy used in this study is as follows: the perturbation scale parameter ui in the variational strategy is larger, the individual vector is randomly selected, and the algorithm tends to perturb the entire population. This accelerates convergence and improves the global optimization-seeking ability of the algorithm.…”
Section: Optimization Of Variation Strategymentioning
confidence: 99%
“…Therefore, numerous studies have developed various multilevel thresholding methods to enhance image segmentation performance and enable practical applications. For example, multilevel thresholding has been widely and successfully applied in areas such as medical diagnosis 11 , 12 , power equipment fault detection 13 , and crop image segmentation 14 .…”
Section: Introductionmentioning
confidence: 99%
“…Metaheuristic algorithms are widely used in many fields to obtain the most effective solutions for various problems. Some of these fields are; image processing [2,3], control techniques [4,5], deep learning models [6,7], machine learning algorithms [8], optimal filter design [9], text clustering [10], feature selection [11], etc. Segmentation is the first and most important step in image processing [12].…”
Section: Introductionmentioning
confidence: 99%