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LCCNN:一种轻量化定制的基于CNN的远程教育应用程序,用于COVID-19识别.

Jiaji Wang1, Suresh Chandra Satapathy2, Shuihua Wang1,3,4

  • 1School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK.

Mobile networks and applications : MONET
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概括
此摘要是机器生成的。

一个新的基于网络的应用程序使用轻量级,定制的卷积神经网络 (LCCNN) 来从CT扫描中准确识别COVID-19. 该工具有助于医学教育和患者诊断,提供了对COVID-19病变的可解释的见解.

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在 COVID-19 疫情中,卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.远程教育就是远程教育.医学影像诊断诊断 医学影像诊断网络应用程序Web应用程序

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 远程教育就是远程教育.

背景情况:

  • 全球疫情凸显了远程教育在教育中的重要性.
  • 医疗诊断和教育需要易于获得和有效的工具,特别是在健康危机期间.
  • 卷积神经网络 (CNN) 在医学图像分析中表现有前途.

研究的目的:

  • 开发一个基于网络的应用程序,用于COVID-19识别,使用新的CNN模型.
  • 评估模型的准确性和诊断能力.
  • 创建一个可解释的AI工具,用于医学教育和患者评估.

主要方法:

  • 提出了一个8层轻量级的定制卷积神经网络 (LCCNN).
  • 实施了五通道数据增强技术,以防止过拟合.
  • 开发了一个用户友好的基于Web的应用程序,集成LCCNN模型.

主要成果:

  • LCCNN模型的准确性达到91.78%,超过了其他八种最先进的方法.
  • 该模型在CT图像中成功检测到COVID-19病变.
  • 生成的热图为病变的清晰可视化提供了可解释性.

结论:

  • 开发的基于网络的应用程序和LCCNN为COVID-19患者提供了有价值的诊断视角.
  • 该工具作为远程医学教育的有效平台,增强学生的互动和理解.
  • LCCNN模型的可解释性有利于放射科医生和教育工作者.