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深度学习辅助的IoMT框架用于大脑微型血液检测.

Zeeshan Ali1, Sheneela Naz2, Sadaf Yasmin3

  • 1Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan.

Heliyon
|December 21, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了人工智能驱动的医疗物联网 (IoMT) 框架,用于准确的脑MRI分析. 增强的UNet模型可以有效地检测大脑微型血流 (CMB),而无需预处理,从而提高诊断准确度.

关键词:
大脑微型血流 (CMB) 的细分.计算机辅助诊断 (CAD) 系统深度学习是一种深度学习.互联网的医疗东西的互联网.联合国网络 联合国网络 联合国网络

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 医疗事物的互联网 医疗事物的互联网

背景情况:

  • 大脑微型血液 (CMB) 很难在脑MRI中检测到,因为它们的小尺寸和与健康组织的相似性.
  • 准确的CMB检测对于诊断和治疗至关重要,特别是在服务不足的地区.
  • 目前用于CMB的计算机辅助诊断 (CAD) 系统涉及多个阶段的过程,阻碍了完全自动化.

研究的目的:

  • 开发一个端到端的医疗物联网 (IoMT) 框架,用于使用脑MRI进行自动脑微血流 (CMB) 检测和细分.
  • 解决现有的CAD系统的局限性,通过提出一种不需要任何处理前或后步骤的模型.
  • 在智能医疗系统中提高CMB检测的准确性和效率.

主要方法:

  • 开发了一种基于UNet的增强深度学习模型,用于从脑MRI扫描中直接检测和细分CMB.
  • 拟议的IoMT框架集成了先进的传感器与人工智能驱动的洞察力,用于实时医学分析.
  • 该模型旨在处理完整的MRI图像,而无需人工干预或复杂的预处理.

主要成果:

  • 拟议的模型获得了0.70的子得分,表明CMB的有效细分.
  • 该系统在CMB检测中显示出高精度 (99%) 和非常低的错误阳性率 (0.002%).
  • 该方法成功检测到CMBs,尽管对比度变化和与正常组织相似,性能优于现有的方法.

结论:

  • 开发的端到端的基于UNet的IoMT框架提供了一个强大的解决方案,用于在脑MRI中准确和自动检测CMB.
  • 这种方法显著提高了诊断能力,特别是在缺乏专业专业知识的环境中.
  • 这些发现为在神经成像方面提供更智能,更可靠的数字医疗服务铺平了道路.