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通过从胸部X射线图像中轻型卷积神经网络使用边缘设备检测COVID-19

Sohamkumar Chauhan¹, Damoder Reddy Edla¹, Vijayasree Boddu²

¹Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, 403401, Goa, India.

BMC medical imaging

|January 3, 2024

概括

这项研究引入了一种新的卷积神经网络 (CNN),用于使用胸部X射线有效检测COVID-19. 拟议的模型实现了高精度,降低了复杂性,使其能够在边缘设备上部署.

科学领域:

人工智能在医学中的应用
对于医学成像的深度学习
计算机视觉在诊断中的应用

背景情况:

深度学习,特别是卷积神经网络 (CNN),是现代临床诊断的关键.
现有的COVID-19检测CNN模型往往以显著的计算复杂性为代价实现高精度.
对于在边缘设备上部署准确而又高效的计算模型的需求至关重要.

研究的目的:

提出一种新的CNN设计,用于从胸部X射线图像中检测COVID-19.
为了实现高检测准确度,减少训练时间和模型复杂度.
促进在边缘计算平台上部署有效的COVID-19诊断工具.

主要方法:

一个新的三级CNN架构,包括预处理,用过器银行进行卷积,以及使用深层卷积层和跳过连接进行特征提取.
胸部X射线图像的训练数据集分为学习 (70%),验证 (10%) 和测试 (20%) 集.
与LMNet,CoroNet,CVDNet和Deep GRU-CNN等已建立的模型进行比较评估.

主要成果:

拟议的模型实现了高精度:培训数据为99.47%,测试数据为98.91%.
卓越的性能指标包括97.54%的精度,98.19%的回忆,99.49%的特异性和97.86%的F1分数.
与其他评估模型相比,模型复杂性显著降低,对过度装配的易感性较低.

结论:

拟议的CNN模型为使用胸部X射线检测COVID-19提供了一个高度准确和计算高效的解决方案.
该模型的复杂性降低和强大的性能使其适合在资源有限的边缘设备上部署.
这种方法解决了基于深度学习的医学诊断中的准确性和复杂性之间的权衡.

关键词:

在美国,CNN是CNN.在 COVID-19 疫情中,胸部X射线图像胸部X射线图像深度学习是一种深度学习.轻量级的模型轻量级的模型.

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