Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

Chen, Jiaochen; Cai, Zhennao; Chen, Huiling; Chen, Xiaowei; Escorcia‐Gutierrez, José; Mansour, Romany F.; Ragab, Mahmoud

doi:10.1007/s42235-023-00365-7

Cited by 23 publications

(6 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In later stages, the Powell mechanism combined with a taboo table enhances local utilization while avoiding excessive time complexity, thereby facilitating global optimization. Therefore, the proposed PSMADE also can try to be applied to more cases in future work, such as renal pathology image segmentation, 104 global optimization, 105 , 106 computer-aided medical diagnosis, 107 , 108 , 109 cancer diagnosis, 110 mental health prediction, 111 and computational image analysis. 112 …”

Section: Resultsmentioning

confidence: 99%

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

Li,

Lin,

et al. 2023

iScience

Self Cite

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

Li,

Lin,

et al. 2023

iScience

Self Cite

View full text Add to dashboard Cite

“…In conclusion, WGWO has the best optimization ability compared to the other five algorithms and can complete higher-quality multi-threshold image segmentation. Of course, it also can be applied to many other fields, such as machine learning models [89], image denoising [90], medical signals [91], structured sparsity optimization [92], renal pathology image segmentation [93], mental health prediction [94], lung cancer diagnosis [95], computer-aided medical diagnosis [96], MRI reconstruction [97], and power distribution network [98].…”

Section: Image Segmentation Experimentsmentioning

confidence: 99%

Face Image Segmentation Using Boosted Grey Wolf Optimizer

Zhang,

Cai,

Xiao

et al. 2023

Biomimetics

Self Cite

View full text Add to dashboard Cite

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur’s entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur’s entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

show abstract

“…Hence, in subsequent investigations, this approach can be extended to a broader range of scenarios, such as optimization of machine learning models [120], computer-aided medical diagnosis [121,122], pathology image segmentation [123][124][125], image denoising [126,127], fine-grained alignment [128], cancer diagnosis [129][130][131], medical signals [132,133], and structured sparsity optimization [134].…”

Section: Plos Onementioning

confidence: 99%

An enhanced decision-making framework for predicting future trends of sharing economy

Wu,

Tang,

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

show abstract

Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

Cited by 23 publications

References 97 publications

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

Face Image Segmentation Using Boosted Grey Wolf Optimizer

An enhanced decision-making framework for predicting future trends of sharing economy

Contact Info

Product

Resources

About