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Updated: Jun 14, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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一个高效的改进的优化器,用于膀癌分类.

Essam H Houssein1, Marwa M Emam1, Waleed Alomoush2

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in biology and medicine
|August 30, 2024
PubMed
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此摘要是机器生成的。

一个改进的优化器 (IPO) 算法提高了膀癌分类的准确性. IPO-SVM方法实现了高性能指标,超过了有效检测膀癌的其他方法.

科学领域:

  • 计算智能是一种计算智能.
  • 医疗信息学医学信息学
  • 机器学习是机器学习.

背景情况:

  • 膀癌 (BC) 具有显著的发病率和死亡率风险.
  • 准确的BC分类具有挑战性,需要专家分析.
  • 像Parrot Optimizer (PO) 这样的现有优化算法存在一些局限性,例如低于最佳的收率和高的错误率.

研究的目的:

  • 开发一个改进的优化算法 (IPO),以克服原PO的局限性.
  • 通过机器学习提高膀癌分类的准确性和效率.
  • 评估拟议的IPO算法的性能与现有方法相比.

主要方法:

  • 通过整合镜像反射学习 (MRL) 和伯努利地图 (BMs) 开发了改进的优化器 (IPO).
  • 评估了CEC 2022测试函数和9个膀癌数据集的IPO.
  • 集成IPO与支持矢量机 (SVM) 分类器,以创建BC分类的IPO-SVM方法.

主要成果:

  • 首次公开募股算法在CEC 2022函数的优化性能方面排名第一.
  • 在BC数据集上,IPO-SVM方法在其他八种元启发算法上表现出优异的性能.
  • IPO-SVM实现了高分类指标:84.11%的准确性,98.10%的敏感性,95.59%的精度,95.98%的特异性和94.15%的F-score.
关键词:
膀癌 (BC) 是一种超音响学 (MH) 是一种超音响学.镜子反射学习 (MRL)优化器 (PO) 是一个软件.支持矢量机器 (SVM) 是一个支持矢量机器.

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结论:

  • 拟议的IPO算法有效地避免了局部最佳,并提高了融合速度和解决方案多样性.
  • IPO-SVM方法为准确的膀癌分类提供了一个有希望和有效的工具.
  • 开发的IPO算法有可能在早期发现和治疗膀癌方面发挥重要作用.