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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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对基于图像的癌症诊断实时深度学习方法的系统审查.

Harini Sriraman1, Saleena Badarudeen1, Saransh Vats1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.

Journal of multidisciplinary healthcare
|September 16, 2024
PubMed
概括
此摘要是机器生成的。

深度学习模型使用医学图像分析显著减少了癌症诊断等待时间. 可解释的人工智能是克服临床应用障碍的关键.

关键词:
在这里,我们可以看到AIAIAI.在美国,CNN是CNN.DL DL 是一个字.人工智能的人工智能是人工智能.这是分类分类的分类.弹性学弹性学 弹性学推进神经网络的前进料医疗保健 医疗保健 医疗保健 医疗保健图像处理是图像处理的过程.机器学习是机器学习.实时诊断的实时诊断

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科学领域:

  • 医疗图像处理 医学图像处理
  • 医疗保健中的人工智能
  • 在瘤学瘤学.

背景情况:

  • 目前的癌症诊断方法需要长时间的等待 (5-30天).
  • 深度学习 (DL) 擅长识别大型数据集中的复杂模式,使其适合用于医学图像分析.
  • 实时医学诊断旨在在一定的时间范围内识别疾病.

研究的目的:

  • 探索深度学习算法的实时癌症诊断的应用.
  • 评估DL对减少诊断等待时间的影响.
  • 评估DL在癌症检测医学成像中的准确性和效率.

主要方法:

  • 在癌症诊断中对DL进行全面的文献综述.
  • 在各种成像模式中评估DL模型准确度和周转时间.
  • 对实时DL诊断的基础设施需求和成本的分析.

主要成果:

  • 深度学习模型,特别是卷积神经网络,在癌症诊断中达到高达99.3%的准确性.
  • DL有可能显著减少癌症诊断的等待期.
  • 评估DL基础设施的有效性和成本效益.

结论:

  • 可解释的DL对于克服临床试验中的一般化问题和数据变化等障碍至关重要.
  • 实施DL可以提高癌症诊断的速度和准确性.
  • 可解释的AI将促进DL在临床癌症诊断中的更广泛采用.