通过基于概率的排名加权聚合:一个优化的联合学习架构来分类呼吸系统疾病
Abdullah Abdul Sattar Shaikh1, M S Bhargavi1, Pavan Kumar C2
1Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India.
Computer methods and programs in biomedicine
|September 30, 2023
概括
这项研究引入了一个使用深度学习和联合学习 (FL) 来检测肺部声音的呼吸系统疾病的AI系统,实现92%的准确性,同时保持患者的隐私. 新的FedWAPR算法提高了用于医学诊断的FL性能.
科学领域:
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
- 医疗信息学 医疗信息学
背景情况:
- 呼吸道疾病是全球主要的健康问题,通常通过主观听觉诊断.
- 目前的诊断方法需要大量的专业知识,容易被误解.
- 医疗数据隐私问题限制了AI诊断工具的开发.
研究的目的:
- 开发基于人工智能的系统,使用肺声音准确地分类呼吸系统疾病.
- 为分布式医疗数据实施保护隐私的联合学习 (FL) 方法.
- 通过一种新的聚合算法来提高FL模型的准确性和效率.
主要方法:
- 利用生成对抗网络 (GAN) 进行数据合成和隐私保护.
- 采用深度学习模型,在合成的肺声音的谱图上进行训练.
- 介绍了一种新的基于概率的排名 (FedWAPR) 算法,用于FL优化.
主要成果:
- 在分类呼吸系统疾病和心力衰竭方面达到约92%的测试准确度.
- 在FL聚合中,FedWAPR算法的表现优于FedAVG.
- 在分散的培训环境中验证了模型的有效性,与集中式模型的准确性相匹配.
结论:
- 这种由人工智能驱动的方法可以通过肺部声音准确,保护隐私地检测肺部疾病.
- FedWAPR显著改善了用于医疗应用的联合学习聚合.
- 这项技术为呼吸系统健康监测提供了安全有效的替代方案.
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