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For: Liu L, Zhao D, Yu F, Heidari AA, Ru J, Chen H, Mafarja M, Turabieh H, Pan Z. Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 2021;138:104910. [PMID: 34638022 DOI: 10.1016/j.compbiomed.2021.104910] [Cited by in Crossref: 23] [Cited by in F6Publishing: 30] [Article Influence: 11.5] [Reference Citation Analysis]
Number Citing Articles
1 Xing J, Zhou X, Zhao H, Chen H, Heidari AA. Elite levy spreading differential evolution via ABC shrink-wrap for multi-threshold segmentation of breast cancer images. Biomedical Signal Processing and Control 2023;82:104592. [DOI: 10.1016/j.bspc.2023.104592] [Reference Citation Analysis]
2 Yang X, Wang R, Zhao D, Yu F, Huang C, Heidari AA, Cai Z, Bourouis S, Algarni AD, Chen H. An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems. Expert Systems with Applications 2023;213:119041. [DOI: 10.1016/j.eswa.2022.119041] [Reference Citation Analysis]
3 Verdicchio M, Brancato V, Cavaliere C, Isgrò F, Salvatore M, Aiello M. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon 2023. [DOI: 10.1016/j.heliyon.2023.e14371] [Reference Citation Analysis]
4 Hao S, Huang C, Heidari AA, Xu Z, Chen H, Althobaiti MM, Mansour RF, Chen X. Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation. Biomedical Signal Processing and Control 2023;80:104139. [DOI: 10.1016/j.bspc.2022.104139] [Reference Citation Analysis]
5 Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. Information Fusion 2023;90:316-352. [DOI: 10.1016/j.inffus.2022.09.031] [Reference Citation Analysis]
6 Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications. Arch Comput Methods Eng 2023;:1-41. [PMID: 36685136 DOI: 10.1007/s11831-023-09883-3] [Reference Citation Analysis]
7 Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med 2023;152:106443. [PMID: 36563539 DOI: 10.1016/j.compbiomed.2022.106443] [Reference Citation Analysis]
8 Li J, Liu K, Hu Y, Zhang H, Heidari AA, Chen H, Zhang W, Algarni AD, Elmannai H. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+. Comput Biol Med 2022;:106501. [PMID: 36635120 DOI: 10.1016/j.compbiomed.2022.106501] [Reference Citation Analysis]
9 Chen H, Li C, Mafarja M, Heidari AA, Chen Y, Cai Z. Slime mould algorithm: a comprehensive review of recent variants and applications. International Journal of Systems Science 2022. [DOI: 10.1080/00207721.2022.2153635] [Reference Citation Analysis]
10 Li H, Wu P, Wang Z, Mao J, Alsaadi FE, Zeng N. A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis. Comput Biol Med 2022;151:106265. [PMID: 36401968 DOI: 10.1016/j.compbiomed.2022.106265] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Tan Z, Tang Y, Li K, Huang H, Luo S. Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning. Swarm and Evolutionary Computation 2022;75:101194. [DOI: 10.1016/j.swevo.2022.101194] [Reference Citation Analysis]
12 Guan Y, Liu H, Huang H, Liang D, Wu S, Zhang T. Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation. Computational and Mathematical Methods in Medicine 2022;2022:1-14. [DOI: 10.1155/2022/1643674] [Reference Citation Analysis]
13 Wang J, Zhu L, Wu B, Ryspayev A. Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization. Forests 2022;13:1746. [DOI: 10.3390/f13111746] [Reference Citation Analysis]
14 Sarkar S, Mali K. Breast Cancer Subtypes Classification with Hybrid Machine Learning Model. Methods Inf Med 2022. [PMID: 36096144 DOI: 10.1055/s-0042-1751043] [Reference Citation Analysis]
15 Gupta S, Su R. An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters. Knowledge-Based Systems 2022;251:109280. [DOI: 10.1016/j.knosys.2022.109280] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Chen Y, Zhou T, Chen Y, Feng L, Zheng C, Liu L, Hu L, Pan B. HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution. Comput Biol Med 2022;149:105981. [PMID: 36029749 DOI: 10.1016/j.compbiomed.2022.105981] [Reference Citation Analysis]
17 Ren L, Zhao D, Zhao X, Chen W, Li L, Wu T, Liang G, Cai Z, Xu S. Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105910] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
18 Shan W, Hu H, Cai Z, Chen H, Liu H, Wang M, Teng Y. Multi-strategies Boosted Mutative Crow Search Algorithm for Global Tasks: Cases of Continuous and Discrete Optimization. J Bionic Eng. [DOI: 10.1007/s42235-022-00228-7] [Reference Citation Analysis]
19 Qi A, Zhao D, Yu F, Heidari AA, Wu Z, Cai Z, Alenezi F, Mansour RF, Chen H, Chen M. Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput Biol Med 2022;148:105810. [PMID: 35868049 DOI: 10.1016/j.compbiomed.2022.105810] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 16.0] [Reference Citation Analysis]
20 Lu SY, Wang SH, Zhang YD. SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection. Comput Biol Med 2022;148:105812. [PMID: 35834967 DOI: 10.1016/j.compbiomed.2022.105812] [Reference Citation Analysis]
21 Shi B, Chen J, Chen H, Lin W, Yang J, Chen Y, Wu C, Huang Z. Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105885] [Reference Citation Analysis]
22 Li L, Qian S, Li Z, Li S. Application of Improved Satin Bowerbird Optimizer in Image Segmentation. Front Plant Sci 2022;13:915811. [DOI: 10.3389/fpls.2022.915811] [Reference Citation Analysis]
23 Wang Y, Huang L, Wu M, Liu S, Jiao J, Bai T. Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105576] [Reference Citation Analysis]
24 Shi B, Zhou T, Lv S, Wang M, Chen S, Heidari AA, Huang X, Chen H, Wang L, Wu P. An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis. Comput Biol Med 2022;146:105529. [PMID: 35594682 DOI: 10.1016/j.compbiomed.2022.105529] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Liu Q, Li N, Jia H, Qi Q, Abualigah L. Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation. Mathematics 2022;10:1014. [DOI: 10.3390/math10071014] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 20.0] [Reference Citation Analysis]
26 Alfadhli J, Jaragh A, Alfailakawi MG, Ahmad I. FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07034-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Yu M, Han M, Li X, Wei X, Jiang H, Chen H, Yu R. Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: Thyroid ultrasound image case study. Comput Biol Med 2022;144:105347. [PMID: 35276549 DOI: 10.1016/j.compbiomed.2022.105347] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 13.0] [Reference Citation Analysis]
28 Chen X, Huang H, Heidari AA, Sun C, Lv Y, Gui W, Liang G, Gu Z, Chen H, Li C, Chen P. An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: A real case with lupus nephritis images. Computers in Biology and Medicine 2022;142:105179. [DOI: 10.1016/j.compbiomed.2021.105179] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
29 Ren L, Heidari AA, Cai Z, Shao Q, Liang G, Chen H, Pan Z. Gaussian kernel probability-driven slime mould algorithm with new movement mechanism for multi-level image segmentation. Measurement 2022;192:110884. [DOI: 10.1016/j.measurement.2022.110884] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
30 Rai R, Das A, Dhal KG. Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. Evolving Systems. [DOI: 10.1007/s12530-022-09425-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
31 Michael E, Ma H, Li H, Qi S, Roldan-valadez E. An Optimized Framework for Breast Cancer Classification Using Machine Learning. BioMed Research International 2022;2022:1-18. [DOI: 10.1155/2022/8482022] [Reference Citation Analysis]
32 Gao H, Liang G, Chen H. Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction. Electronics 2022;11:209. [DOI: 10.3390/electronics11020209] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
33 Su H, Zhao D, Yu F, Heidari AA, Zhang Y, Chen H, Li C, Pan J, Quan S. Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images. Comput Biol Med 2022;142:105181. [PMID: 35016099 DOI: 10.1016/j.compbiomed.2021.105181] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 28.0] [Reference Citation Analysis]
34 Ahmed S, Biswas A. A Survey on Multilevel Thresholding-Based Image Segmentation Techniques. Futuristic Trends in Networks and Computing Technologies 2022. [DOI: 10.1007/978-981-19-5037-7_59] [Reference Citation Analysis]
35 Chen C, Wang X, Heidari AA, Yu H, Chen H. Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu. Front Plant Sci 2021;12:789911. [PMID: 34966405 DOI: 10.3389/fpls.2021.789911] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
36 Lin S, Jia H, Abualigah L, Altalhi M. Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures. Entropy (Basel) 2021;23:1700. [PMID: 34946006 DOI: 10.3390/e23121700] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]