通过CT扫描和胸部X射线图像,为COVID-19预测组建深度蜂蜜架构
B Bhaskar Reddy1, M Venkata Sudhakar2, P Rahul Reddy3
1ECE Department, St. Peters Engineering College, Hyderabad, Telangana India.
概括
这项研究引入了一个集体深度学习模型,用于使用胸部X射线 (CXR) 和CT扫描快速检测COVID-19. 这种新的方法实现了高精度,有助于快速和经济有效地查传染病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 由于COVID-19的流行,需要快速且低成本的诊断工具.
- 放射性成像,特别是胸部X射线 (CXR) 和计算机断层扫描 (CT),提供了可访问的查方法.
- 现有的诊断方法需要提高速度和准确性,以有效应对流行病.
研究的目的:
- 利用CXR和CT图像开发一种新的集体深度学习模型,用于准确的COVID-19预测.
- 通过有效的预测模型,提高COVID-19诊断的性能和稳定性.
- 提高对受影响个人的COVID-19查效率.
主要方法:
- 应用了图像预处理技术,包括缩放和中间选.
- 使用翻转和旋转等数据增强策略来改善模型通用化.
- 一个集团深度蜂蜜架构 (EDHA) 结合了ShuffleNet,SqueezeNet和DenseNet-201,通过蜂蜜子算法 (HBA) 进行了优化,用于分类.
主要成果:
- 拟议的EDHA模型与现有方法相比,显示出更高的性能.
- 在CXR数据集上实现了高精度 (99.1%),灵敏度 (99%),特异性 (98.6%),精度 (99.6%),F1得分 (98.9%),MCC (99.2%) 和AUC (0.98).
- 该模型为CXR数据集实现了820秒的计算时间.
结论:
- 开发的EDHA模型提供了一个高度准确和高效的解决方案,用于使用放射性图像检测COVID-19.
- 整体方法和HBA优化有助于强大的和改进的诊断性能.
- 这种人工智能驱动的工具可以显著帮助COVID-19患者的快速和经济有效的查.
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