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增强的多类乳腺癌分类从全幻灯片组织病理学图像使用建议的深度学习模型.

Adnan Rafiq1, Arfan Jaffar1, Ghazanfar Latif2,3

  • 1Department of Computer Science & IT, Superior University, Lahore 54000, Pakistan.

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概括

使用DenseNet121的深度学习模型在从组织学图像中分类乳腺癌方面取得了高准确性. 这项技术有望改善乳腺癌诊断和治疗计划.

关键词:
乳腺癌的分类 乳腺癌的分类深度特征 功能 功能 功能.深度学习是一种深度学习.深度神经网络是一个神经网络.密集的网络121有不同的放大级别.基因病理学图像 基因病理学图像

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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
  • 在瘤学中使用人工智能

背景情况:

  • 乳腺癌是全球癌症死亡的主要原因之一.
  • 准确的组织学分类对于有效的乳腺癌诊断和治疗至关重要.

研究的目的:

  • 开发和评估用于乳腺癌检测和分类的深度学习模型.
  • 为了评估模型在全幻灯片组织病理学图像上的表现.

主要方法:

  • 提出了一个基于DenseNet121的深度学习模型.
  • 实验是使用BreakHis数据集的基因病理图像进行的.

主要成果:

  • 该模型实现了98.50%的准确性和0.98AUC对二进制分类.
  • 对于多类分类,该模型获得了92.50%的准确性和0.94 AUC.

结论:

  • 深度学习模型在区分良性瘤和恶性瘤方面表现出卓越的性能.
  • 这项研究强调了人工智能在促进乳腺癌诊断方面的潜力.