FedPneu:跨多客户跨罗医疗保健数据库的肺炎检测联合学习
Shagun Sharma1, Kalpna Guleria1, Ayush Dogra1
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
这项研究介绍了FedPneu,这是用于肺炎检测的联合深度学习模型. 双客户端架构实现了最高的准确性,提高了医疗成像分析中的隐私.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 肺炎是全球主要的死亡原因之一.
- 传统的医学成像深度学习引发了数据隐私方面的担忧.
- 联合学习提供了一种去中心化的方法来缓解隐私风险.
研究的目的:
- 开发和评估用于肺炎检测的联合深度学习模型.
- 评估FedPneu模型在不同客户端架构上的性能.
- 通过医学成像来提高肺炎预测中的患者数据隐私.
主要方法:
- 使用联合学习框架实施FedPneu模型.
- 在X射线图像上应用该模型用于早期肺炎检测.
- 用2,3,4和5个客户端对模型性能进行评估,配置学习率和时代等参数.
主要成果:
- 双客户 FedPneu 架构实现了最高准确率 85.632%.
- 3,4 和 5 个客户端的准确率分别为 85.536%, 76.112% 和 74.123%.
- 两个客户端的设置在肺炎检测方面表现出卓越的性能.
结论:
- 两个客户端的FedPneu架构是最佳的隐私保护的肺炎检测.
- 与多个单元数据集的联合学习增强了患者数据的隐私.
- 费德普纽 (FedPneu) 模型提供了更好的预测结果,同时保护了敏感的健康信息.
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