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成功史 适应性竞争性群体优化器与线性人口减少:性能基准测试和应用在眼病检测中的应用.

Rui Zhong1, Zhongmin Wang2, Abdelazim G Hussien3

  • 1Information Initiative Center, Hokkaido University, Sapporo, Japan.

Computers in biology and medicine
|January 3, 2025
PubMed
概括

一个新的优化器,成功史适应性竞争群体优化器与线性人口减少 (L-SHACSO),提高了人工智能 (AI) 的眼睛疾病检测. 这种先进的优化器可以提高医疗诊断中的预测模型准确性.

关键词:
竞争性群体优化器 (CSO)眼部疾病检测 眼部疾病检测线性人口减少 线性人口减少超启发式算法 (MAs) 是一种算法.成功史 适应 适应

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科学领域:

  • 人工智能的人工智能
  • 计算优化计算优化
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 人工智能 (AI) 已经推进了眼睛疾病检测,但模型准确性受到优化器缺陷的限制.
  • 现有的优化人员在复杂的优化任务中努力有效地平衡探索和开发.

研究的目的:

  • 引入一个高效的优化器,成功史适应性竞争性群体优化器与线性人口减少 (L-SHACSO),以提高AI模型的性能.
  • 评估L-SHACSO在基准和工程问题上与最先进的优化器相比的优势.
  • 应用L-SHACSO来提高眼病检测模型的准确性.

主要方法:

  • 通过将成功历史适应和线性人口减少策略集成到竞争性群体优化器 (CSO) 中,开发了L-SHACSO.
  • 在CEC2017,CEC2020,CEC2022和八个工程问题上进行了广泛的数值实验,比较L-SHACSO与jSO,L-SHADE-cnEpSin,RIME和Parrot Optimizer (PO).
  • 集成L-SHACSO与DenseNet和极端学习机器 (ELM) 创建DenseNet-L-SHACSO-ELM眼睛疾病分类模型.

主要成果:

  • 与竞争的最先进的算法相比,L-SHACSO在各种优化任务中表现出卓越的性能.
  • 在公共数据集上,DenseNet-L-SHACSO-ELM模型在检测眼病方面取得了高准确性.
  • 拟议的模型证实了L-SHACSO在实际医疗应用中的可行性和有效性.

结论:

  • L-SHACSO是一种高效的优化器,可以显著提高AI模型的性能,特别是在医疗图像分析等复杂任务中.
  • 将L-SHACSO集成到深度学习模型中,为准确可靠的眼病检测提供了一个有希望的方法.
  • 开发的L-SHACSO优化器具有在医疗保健及其他领域的实际应用的巨大潜力.