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相关实验视频

Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于EfficientNet-B3的自动深度学习框架,用于多类内镜膀组织分类.

A A Abd El-Aziz1, Mahmood A Mahmood1, Sameh Abd El-Ghany1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
概括
此摘要是机器生成的。

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这项研究引入了用于膀癌检测的自动化深度学习系统. EfficientNet-B3模型准确地对光滑肌肉图像进行分类,有助于早期诊断并降低医疗保健成本.

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:

  • 膀癌 (BLCA) 诊断是具有挑战性的,因为瘤异质性和复杂的组织病理学.
  • 组织的手动分类耗时,容易出现错误,缺乏标准化.
  • 对于自动化,可靠的系统来实现高效的BLCA检测是非常需要的.

研究的目的:

  • 开发和验证一个深度学习模型,用于自动早期检测膀癌.
  • 与传统方法相比,提高BLCA诊断的准确性和效率.
  • 为了减少进行膀癌查的患者的诊断时间和成本.

主要方法:

  • 使用EfficientNet-B3模型的深度学习方法被用于多类分类.
  • 使用了内镜膀组织分类 (EBTC) 数据集,并进行了预处理,包括调整大小和正常化.
  • 进行了五倍交叉验证和消去研究,以优化超参数,并与其他领先的DL模型对性能进行验证.

主要成果:

  • EfficientNet-B3模型实现了高性能指标:准确率为99.03%,特异性为99.30%,精度为97.95%,回忆率为96.85%,F1-score为97.36%.
  • 拟议的模型在分类膀组织图像方面明显优于其他五个领先的深度学习模型.
关键词:
有效的Net-B3 有效的Net-B3内镜膀组织分类数据集.膀癌:膀癌是一种癌症.深度学习是一种深度学习.内镜图像的内镜图像.

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  • 该系统证明了有效和准确的膀癌组织的识别.

    • 结论:

    • EfficientNet-B3模型显示出作为准确和高效的膀癌诊断的宝贵工具的巨大潜力.
    • 使用这种DL模型进行自动分类可以简化诊断过程,从而及时进行干预.
    • 这项技术为减少与膀癌相关的发病率和死亡率提供了一个有希望的解决方案.