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使用全光学深度衍射神经网络进行肺结节检测和分类.

Junjie Shao1, Lingxiao Zhou1, Sze Yan Fion Yeung2

  • 1Nanophotonics Research Center, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China.

Life (Basel, Switzerland)
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

一个深度衍射神经网络 (D2NN) 显示出肺癌检测的希望. 这种光学计算方法在CT扫描中准确识别肺结节,有助于早期诊断.

关键词:
辅助诊断是一种辅助诊断.所有的光学都是光学.深度衍射神经网络是一个深度衍射神经网络.肺部结节 肺部结节 肺部结节实时实时的时间.

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科学领域:

  • 光学是什么?光学是什么?光学是什么?
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 深度衍射神经网络 (D2NN) 是用于图像分类和逻辑操作的先进光学计算结构.
  • 计算机断层扫描 (CT) 图像是检测和分析肺结节的标准和可靠技术.
  • 肺癌的诊断在很大程度上依赖于从CT扫描中准确识别和分类肺结节.

研究的目的:

  • 调查全光学D2NN在CT图像中检测和分类肺结节的有效性,以诊断肺癌.
  • 评估D2NN在识别结节的存在和区分良性和恶性类型方面的性能.

主要方法:

  • 一个全光学深衍射神经网络 (D2NN) 被设计和实施.
  • D2NN使用LIDC-IDRI数据集进行了训练,这是一个全面的肺结节CT图像集合.
  • 性能评估是在一个由CT图像组成的单独测试集上进行的.

主要成果:

  • 对于肺结节的检测,D2NN在CT图像中识别结节的存在时实现了91.08%的回忆率.
  • 在肺结节分类中,D2NN区分了良性和恶性结节,准确度为76.77%,AUC为0.8292.

结论:

  • 该研究表明,采用光学神经网络用于快速医疗图像处理的可行性.
  • 全光D2NN为开发更快,人工智能驱动的放射学辅助诊断工具提供了潜在的途径.
  • 这种方法有望提高肺癌查和诊断的效率和准确性.