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使用BAT算法优化的机器学习模型检测口腔癌,并使用转移学习和随机抽样.

Sakinat O Folorunso1, Akinshipo Abdulwarith2, Abidemi Emmanuel Adeniyi3

  • 1Artificial Intelligence Systems Research Group, Department of Computer Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria.

Computers in biology and medicine
|May 6, 2025
PubMed
概括

这项研究引入了一种新的AI框架 (TR-ROS-BAT-ML),用于改进口腔癌的检测. 该系统有效地处理不平衡的数据,实现高诊断回忆,用于早期病变识别.

关键词:
在BAT算法算法中,使用BAT算法.合唱团组合在一起.口腔癌是口腔癌的一个形式.随机过量抽样是随机过量抽样.转移学习转移学习

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科学领域:

  • 医疗成像医学成像
  • 计算病理学计算病理学
  • 医疗保健中的人工智能

背景情况:

  • 口腔癌是一个重要的公共卫生问题,需要准确的早期检测.
  • 现有的诊断方法在特征选择,不平衡的数据集和计算效率方面扎.

研究的目的:

  • 开发一种新的诊断框架 (TR-ROS-BAT-ML) 来提高口腔癌的检测.
  • 整合转移学习,随机过量抽样,BAT算法优化和整体机器学习.

主要方法:

  • 利用了1224张正常口腔表皮和OSCC的H&E染色组织学图像的数据集.
  • 雇员预先训练有素的深度学习模型用于特征提取和随机过量抽样以寻找类不平衡.
  • 应用了BAT算法用于特征选择和超参数调整,然后进行组合分类.

主要成果:

  • TR-ROS-BAT-ML框架显示了高诊断性能,优化的额外树木 (ET) 模型实现了0.992.99的召回率.
  • 该框架有效地处理不平衡的数据集,并优化了分类性能.
  • EfficientNetV2S + ROS + MLP组合的结果是最低的准确率为50.8%.

结论:

  • 这项研究验证了结合自然灵感优化,转移学习和整体机器学习用于口腔癌检测的有效性.
  • TR-ROS-BAT-ML框架提供了一个可扩展,准确和高效的AI诊断工具.
  • 未来的工作将探索多模式数据集成,以提高临床适用性.