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一种轻量级的深度学习方法用于检测COVID-19使用X射线图像与边缘联合会

Sohaib Bin Khalid Alvi^1,2, Muhammad Ziad Nayyer¹, Muhammad Hasan Jamal³

¹Department of Computer Science, GIFT University, Gujranwala, Pakistan.

|October 6, 2023

概括

本研究介绍了一种轻量级的边缘联合学习模型,用于使用X射线图像检测COVID-19. 该架构优化了资源配置,大大减少了培训时间,并增强了有效医疗诊断的隐私.

科学领域:

人工智能的人工智能
计算机科学计算机科学
医疗成像医学成像

背景情况:

联合学习和边缘计算为保护隐私的分布式机器学习提供了解决方案.
使用医学成像检测COVID-19需要高效和准确的计算模型.
现有的方法在平衡性能,隐私和资源利用方面经常面临挑战.

研究的目的:

开发基于轻量级卷积神经网络 (CNN) 的边缘联合学习架构,用于从X射线图像中检测COVID-19.
为了最大限度地降低边缘AI系统中的计算成本,延迟和带宽要求.
通过分布式模型培训来增强患者的隐私.

主要方法:

一个边缘联合学习架构与智能经纪人,以基于资源可用性和近距离优化任务卸载.
利用DenseNet (深度60,357,482个参数) 进行COVID-19检测模型培训.
实施资源意识的分布式方法,以优化计算资源利用率.

主要成果:

培训时间减少了53.1%.
优化了CPU和内存利用率,分别为17.5%和33.6%.
保持了高的COVID-19检测准确性,保持了F1的得分.

结论:

拟议的轻量级的基于CNN的边缘联合学习架构是有效和有效的COVID-19检测.
该方法提供了一个全面的解决方案,解决隐私问题,负载平衡和能源优化,同时减少培训时间.
这种整体模型提供了一个平衡的系统,在边缘AI应用中提供最佳性能.

关键词:

公共卫生公共卫生深度学习是一种深度学习.边缘计算是一种边缘计算.联合学习的联合学习

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