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Updated: Jan 18, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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适应和迁移增强的树种算法用于多门CT图像细分和肺癌识别.

Chenxi Li1,2, Jianhua Jiang1,2, Zhixing Ma1,2

  • 1Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun, China.

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|January 16, 2026
PubMed
概括

适应和迁移增强的树种算法 (AMTSA) 在复杂的优化任务中改进了原来的树种算法 (TSA). 在高维问题和医学图像分析方面,AMTSA表现出卓越的性能.

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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 群集情报 群集情报 群集情报

背景情况:

  • 原始的树种算法 (TSA) 在高维优化中遭受过早的融合和局部优化.
  • 现有的TSA变种在有效解决复杂的大规模问题方面存在局限性.

研究的目的:

  • 引入适应和迁移增强的树种算法 (AMTSA),以克服标准TSA的局限性.
  • 在复杂的优化任务中增强全球勘探,适应性和融合稳定性.

主要方法:

  • 实施了自适应树迁移机制,以根据个人健康状况进行动态步骤大小和方向调整.
  • 引入了使用动态韦布尔分布进行灵活搜索控制的自适应种子生成策略.
  • 整合了一种非线性步骤大小调整函数,灵感来自GBO算法,以提高汇稳定性.

主要成果:

  • 在IEEE CEC 2014基准函数上,AMTSA的性能优于最先进的优化器 (JADE,LSHADE) 和TSA变体 (STSA,fb-TSA,MTSA).
  • 在高维测试中 (30,50,100维),AMTSA取得了最佳的优化结果和最快的融合.
  • 一个AMTSA-SVM模型在肺癌CT图像细分中实现了89.5%的准确性,超过了其他方法.

结论:

  • 拟议的AMTSA通过自适应迁移,动态种子生成和非线性步骤大小控制有效地解决了TSA的缺陷.
  • 对于高维和复杂的优化问题,AMTSA提供了更高效和更强大的解决方案.
  • 增强的算法显示了在现实世界的应用,如医疗图像分析显著的潜力.