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改进的salp swarm算法驱动的深度CNN用于脑瘤分析.

Umang Kumar Agrawal1, Nibedan Panda2, Ghanshyam G Tejani3,4

  • 1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India.

Scientific reports
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PubMed
概括
此摘要是机器生成的。

当地搜索SSA (LS-SSA) 通过平衡勘探和开发,改进了标准的Salp Swarm算法 (SSA). 这种增强的元启发算法有效调整卷积神经网络 (CNN) 用于医疗成像任务.

关键词:
大脑MRI 脑部MRI 脑部在美国,CNN是CNN.当地搜索 地方搜索医学成像医学成像预测 预后 预测 预测这就是SSA SSA.

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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
  • 机器学习 机器学习

背景情况:

  • 基于集群的元启发算法,如Salp集群算法 (SSA),需要在探索和利用之间取得平衡,以获得最佳性能.
  • 标准SSA可能会遭受过早的收,并因这些运营商的不平衡而陷入局部最小值.
  • 这种不平衡限制了SSA在微调复杂任务的参数方面的有效性,例如医学成像中的卷积神经网络 (CNN) 超参数优化.

研究的目的:

  • 引入一种新的混合算法,局部搜索SSA (LS-SSA),旨在克服标准SSA的局限性.
  • 在SSA框架内加强勘探和开采平衡.
  • 评估LS-SSA在改善CNN超参数调用于医学成像分析方面的有效性.

主要方法:

  • 拟议的本地搜索SSA (LS-SSA) 算法是通过将本地搜索机制集成到标准SSA. developed中来开发的.
  • 通过IEEE-CEC-2017基准套件中的28个功能,对LS-SSA的性能进行了严格的评估.
  • 通过一系列非参数测试来确定统计意义,将LS-SSA与当代方法进行比较.

主要成果:

  • 与现有方法相比,LS-SSA在各种基准函数中表现出优越的性能.
  • 该算法有效地解决了标准SSA固有的过早收和局部最小值问题.
  • 对脑MRI数据集的CNN超参数调整的应用显示了更高的准确性,更低的标准偏差,更低的最小RMSE和更高的平均性能.

结论:

  • 与标准的SSA相比,LS-SSA为优化问题提供了更强大,更有效的方法.
  • 增强的算法实现了更快的全球最佳趋同,产生更好的候选解决方案.
  • LS-SSA是一种高效的工具,用于优化医学成像中CNN超参数,特别是用于脑MRI分析,从而显著改善模型性能.