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基于组织病理的前列腺癌分类使用ResNet:一个全面的深度学习分析.

Declan Ikechukwu Emegano1,2, Mubarak Taiwo Mustapha3, Dilber Uzun Ozsahin3,4,5

  • 1Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey. declanikechukwu.emegano@neu.edu.tr.

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概括

这项研究利用ResNet50卷积神经网络 (CNN) 来准确地从组织学图像中分类前列腺癌. 该模型实现了高性能,证明了其提高诊断准确性和患者结果的潜力.

关键词:
良性 良性的活检是为了检查生物质.历史学 历史学 历史学恶性 恶性 恶性前列腺癌是什么意思 前列腺癌是什么意思在ResNet50中使用ResNet50

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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:

  • 前列腺癌是全球男性死亡的主要原因之一.
  • 准确及时诊断对于优化患者的治疗结果至关重要.
  • 组织病理图像分析是前列腺癌诊断的关键.

研究的目的:

  • 评估ResNet50卷积神经网络 (CNN) 对于从组织学图像中分类前列腺癌的有效性.
  • 评估ResNet50在区分良性和恶性前列腺组织的诊断性能.
  • 将ResNet50的性能与其他深度学习模型进行比较.

主要方法:

  • 使用ResNet50架构分析了1276张前列腺活检图像的数据集.
  • 训练ResNet50模型将图像分类为良性或恶性.
  • 使用包括准确性,精度,回忆和F1分数在内的指标来评估性能.

主要成果:

  • ResNet50表现出卓越的性能,准确度高 (良性为0.98,恶性为0.99).
  • 精度,回忆和F1分数在良性和恶性分类中始终很高.
  • 该模型显示了比MobileNet和CNN-RNN的性能增长,准确度的95%CI为 (0.91,1.00).

结论:

  • ResNet50模型显示出从组织学图像中准确分类前列腺癌的巨大潜力.
  • 通过与最先进的深度学习模型进行比较,证实了该模型的稳定性.
  • 这种人工智能工具的临床整合可以增强决策和改善前列腺癌管理中的患者结果.