使用卷积神经网络和转移学习的X射线图像进行增强的COVID-19检测
Qanita Bani Baker1, Mahmoud Hammad1, Mohammed Al-Smadi2
1Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Journal of imaging
|October 25, 2024
概括
这项研究开发了一种使用卷积神经网络 (CNN) 来从胸部X射线 (CXR) 图像中检测冠状病毒 (COVID-19) 的自动化系统. Xception模型实现了高精度,显示了快速,大规模疾病查的前景.
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:
- 全球冠状病毒 (COVID-19) 疫情需要快速和可扩展的诊断工具.
- 早期检测COVID-19对于有效的疾病控制和患者管理至关重要.
- 对胸部X射线 (CXR) 图像的自动分析为大规模查提供了一个有希望的途径.
研究的目的:
- 通过先进的卷积神经网络 (CNN) 模型引入一种用于自动检测COVID-19的新方法.
- 用CXR图像评估六个不同的CNN架构在二进制和多类分类任务中的性能.
- 从放射数据中确定最有效的CNN模型,以准确地诊断COVID-19.
主要方法:
- 利用15,000张胸部X射线 (CXR) 图像的数据集进行培训和验证.
- 采用了六种最先进的CNN模型:Xception,Inception-V3,ResNet50,VGG19,DenseNet201,以及InceptionResNet-V2. 这三种模型均为最先进的CNN模型.
- 进行了二进制 (正常与异常) 和多类 (正常,COVID-19,肺炎) 的分类.
主要成果:
- 该Xception模型实现了卓越的性能,在二进制分类中准确度为98.13%.
- 在多类分类中,Xception模型达到87.73%的准确率.
- 其他模型,如ResNet50,也表现出了具有竞争力的结果,验证了CNN的方法.
结论:
- 卷积神经网络模型,特别是Xception,在自动检测CXR图像的COVID-19中显示出高效率.
- 开发的自动化系统有可能在大规模查和疫情控制工作中显著帮助.
- 这种人工智能驱动的方法为早期诊断COVID-19等呼吸道疾病提供了可扩展和高效的解决方案.
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