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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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使用修改的惯性投影算法进行乳腺癌查,用于分割可行性问题.

Pennipat Nabheerong1, Warissara Kiththiworaphongkich2, Watcharaporn Cholamjiak3

  • 1Radiology Department, School of Medicine, University of Phayao, Phayao 56000, Thailand.

International journal of breast cancer
|September 18, 2023
PubMed
概括

这项研究引入了一种新的算法,用于乳腺癌检测在乳房扫描,增强极端学习机器. 与现有的机器学习模型相比,新方法显示出更高的性能.

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科学领域:

  • 医疗成像医学成像
  • 机器学习 机器学习
  • 优化算法 优化算法

背景情况:

  • 早期发现乳腺癌对于有效治疗至关重要.
  • 乳房扫描查是早期检测的主要工具.
  • 优化机器学习模型可以提高诊断准确性.

研究的目的:

  • 在乳腺癌检测中开发和验证极端学习机器 (ELM) 的修改优化算法.
  • 提高乳腺癌检测的准确性和效率,使用乳房影像.
  • 证明拟议的算法在现有方法上的优越性.

主要方法:

  • 用曼的代对分割可行性问题的惯性放松CQ算法的修改.
  • 修改算法的应用作为一个优化器在极端学习机器框架内.
  • 严格的数学证明在温和条件下,拟议的算法的弱收.

主要成果:

  • 拟议的算法实现了高性能指标:准确率为85.03%,精度为82.56%,回忆率为87.65%,F1-score为85.03%.
  • 对比分析表明,新算法在乳腺癌检测方面优于其他机器学习模型.
  • 该算法的有效性被验证为乳房镜查应用.

结论:

  • 修改后的惯性放松CQ算法为基于ELM的乳腺癌检测提供了显著的进步.
  • 这种优化技术可以提高乳房扫描查的诊断性能.
  • 这项研究强调了先进的优化方法在改善医学成像分析方面的潜力.