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一个基于C-19的多模型查框架,使用深度学习启发的数据融合.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能的人工智能
    • 生物医学工程 生物医学工程

    背景情况:

    • 医疗物联网 (IoMT) 和边缘计算的兴起增强了远程医疗监控.
    • 现有的使用RCNN,FTE和可穿戴传感器的COVID-19查模型是计算密集的,不适合轻量级环境.

    研究的目的:

    • 提出一种新的多式联络查框架,利用深度学习启发的数据融合来改善远程COVID-19监测.
    • 提高医疗保健应用中的轻量级环境的计算效率和适用性.

    主要方法:

    • 一个变化编码器 (VEN) 使用YoLo识别的感兴趣区域 (RoI) 测量皮肤温度.
    • 一个多数据融合模型将电子健康记录与可穿戴传感器数据集成在一起.
    • 数据减少机制优化了计算效率,而附带概率方法估计了特征权重.

    主要成果:

    • 拟议的框架在实验室数据集上达到95.2%的精度.
    • 与最先进的方法相比,该模型显示出更高的性能.
    • 该设计有效地融合多式联运数据,预测特征重量,并选择相关特征.

    结论:

    • 多式联接查框架为远程COVID-19监测提供了计算效率高,高度准确的解决方案.
    • 深度学习,数据融合和功能优化的集成增强了IoMT应用程序的选能力.
    • 这种方法提供了一种强大的方法,用于使用热和感官数据评估异常的COVID-19实例.