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基于平衡优化的集体CNN框架用于乳腺癌多类分类,使用组织病理图像.

Yasemin Çetin-Kaya1

  • 1Department of Computer Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpasa University, Tokat 60250, Turkey.

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概括

一种新的深度学习模型MultiHisNet,通过组织病理图像准确诊断八种乳腺癌,有助于早期检测并改善患者的治疗结果.

科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 乳腺癌仍然是女性死亡的主要原因之一.
  • 早期检测和准确诊断对于有效治疗和降低死亡率至关重要.
  • 组织病理学图像对于乳腺癌的诊断和分期至关重要.

研究的目的:

  • 开发一种高性能深度学习模型,用于诊断八种类型的乳腺癌.
  • 解决图像分析的挑战,包括数据不平衡和模型过拟合.
  • 提出一个新的模型,MultiHisNet,用于改进乳腺癌分类.

主要方法:

  • 利用了BreakHis数据集中的基因病理图像.
  • 微调了20个最先进的深度学习模型.
  • 开发和评估了20个定制模型,包括新的MultiHisNet.Net.
  • 使用表现最佳的模型和平衡优化器创建了一个集合模型.

主要成果:

  • 新的MultiHisNet模型在多类乳腺癌分类中实现了94.69%的准确性.
  • 拟议的整体模型在八类乳腺癌检测中达到96.71%的准确性.
  • 该研究确定了有效模型性能的关键组件,例如注意力模块和剩余链接.
关键词:
乳腺癌的分类 乳腺癌的分类深度学习是一种深度学习.组合学习组合学习平衡优化器是一个平衡优化器.组织病理学图像 组织病理学图像

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结论:

  • 开发的深度学习模型,特别是集体方法,在乳腺癌诊断中表现出高准确性.
  • 这些发现表明,拟议的模型可以作为一个有价值的工具来协助病理学家.
  • 这项研究有助于通过人工智能推进自动化乳腺癌诊断.