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用各种优化深度学习方法对乳腺癌进行分类.

Mustafa Güler1, Gamze Sart2, Ömer Algorabi3

  • 1Engineering Sciences Department, Engineering Faculty, Istanbul University-Cerrahpasa, Istanbul 34320, Türkiye.

Diagnostics (Basel, Switzerland)
|July 29, 2025
PubMed
概括

深度学习模型对乳腺癌分类有希望. DenseNet201实现了89.4%的准确性,证明了AI的准确性.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.图像处理是图像处理的过程.瘤是一个瘤.

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科学领域:

  • 医疗数据分析 医疗数据分析
  • 图像处理 图像处理
  • 医学中的人工智能

背景情况:

  • 乳腺癌发病率的增加需要先进的诊断工具.
  • 区分良性瘤和恶性瘤对于患者的治疗结果至关重要.
  • 医学中的深度学习应用已经取得了显著的成功.

研究的目的:

  • 评估11个深度学习算法的乳腺癌分类的有效性.
  • 为了比较各种深度学习模型在病态乳腺活检图像上的性能.
  • 确定最准确的深度学习模型来分类良性和恶性乳腺瘤.

主要方法:

  • 使用了11个深度学习算法,包括ResNet,VGG16,DenseNet201等.
  • 分类了1万张乳腺活检图像 (6172张良性,3828张恶性).
  • 雇佣了80%的培训,10%的验证和10%的测试数据分割.

主要成果:

  • DenseNet201实现了最高的分类准确率,达到89.4%.
  • DenseNet201表现出强的表现,精度为88.2%,回忆率为84.1%,F1得分为86.1%.
  • 在DenseNet201.1.中,AUC得分为95.8%.

结论:

  • 深度学习算法对准确的乳腺癌分类具有重大潜力.
  • 在这个任务中,DenseNet201成为了一个非常有效的模型.
  • 未来的研究应该探索多模式数据集成和组合方法,以提高临床适用性.