基于神经网络的智能电子健康应用程序用于使用无服务器计算预测结核病
IEEE journal of biomedical and health informatics
|February 20, 2024
概括
本研究介绍了一个智能电子健康应用程序,使用神经网络来预测结核病 (TB). VGG-19模型表现出卓越的性能,突出了机器学习在增强物联网和电子健康系统的疾病预测方面的潜力.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 物联网 (IoT) 和电子健康记录 (e-health) 的整合正在改变医疗诊断和治疗.
- 早期预测像结核病 (TB) 这样的疾病对于有效的管理和改善患者的治疗结果至关重要.
研究的目的:
- 提出一种基于神经网络的智能电子健康应用程序,用于使用无服务器计算预测结核病 (TB).
- 评估各种卷积神经网络 (CNN) 架构在肺部图像中检测结核病的性能.
- 为了比较医疗保健中的机器学习模型的服务器和无服务器部署策略.
主要方法:
- 使用转移学习训练和验证了Densenet-201,VGG-19和Mobilenet-V3-小CNN架构.
- 评估模型性能,使用准确性,损失,交叉与结合,精度,回忆和F1分数等指标.
- 使用JMeter评估部署性能,以测量服务器和无服务器环境中的响应率,吞吐量和错误率.
主要成果:
- 在对结核病预测进行评估的CNN模型中,VGG-19架构实现了最佳性能.
- 无服务器部署策略在将ML模型集成到电子健康系统中显示出有希望的结果.
- 性能指标表明,在支持物联网的医疗保健框架内,使用CNN用于早期结核病检测的可行性.
结论:
- 机器学习模型,特别是VGG-19,对提高物联网和电子健康系统在预测结核病方面具有重大前景.
- 无服务器计算为人工智能驱动的医疗保健应用提供了高效的部署选择.
- 这项研究为开发数据驱动的智能医疗解决方案提供了基础,以改善疾病诊断和治疗.
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