从胸部X射线图像中检测COVID-19使用转移学习.
1Systems and Information Department, National Research Centre, Dokki, 12311, Cairo, Egypt. enas_mfahmy@yahoo.com.
Scientific reports
|May 21, 2024
概括
这项研究引入了使用胸部X射线用于早期COVID-19诊断的深度学习框架. 该系统实现了高精度,有助于快速识别和隔离感染个体.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- COVID-19是一种高度传染性的冠状病毒,对全球健康构成重大风险.
- 目前对COVID-19的诊断方法,如喉和鼻子抽样,在灵敏度上有局限性,容易出现错误.
- 早期诊断对于及时隔离疑似病例和防止进一步传播至关重要.
研究的目的:
- 通过胸部X射线图像提出用于早期COVID-19诊断的深度学习框架.
- 通过图像预处理技术提高卷积神经网络 (CNN) 模型的分类性能.
- 评估预先训练有素的CNN模型,特别是VGG19和EfficientNetB0在COVID-19检测中的有效性.
主要方法:
- 一个两阶段的框架,涉及图像预处理和使用转移学习进行分类.
- 应用各种图像增强技术,以完整和细分的X射线图像.
- 使用预先训练的VGG19和EfficientNetB0模型进行二进制和四类分类任务.
主要成果:
- 使用增强的全X射线图像,VGG19模型实现了0.96的灵敏度,0.94的特异性,0.9412的精度,0.9505的F1得分和0.95的二进制分类准确度.
- 拟议的框架显示了一个有希望的分类准确度为0.935的4类分类任务.
- 图像增强技术提高了CNN模型的分类性能.
结论:
- 开发的深度学习框架显示了从胸部X射线图像中早期和准确诊断COVID-19的巨大潜力.
- 使用转移学习与预先训练有素的CNN模型,如VGG19,为医疗图像分类任务提供了有效的方法.
- 这种人工智能驱动的方法可以支持医疗保健专业人员更快地做出诊断决策,从而改善患者的治疗结果和公共卫生措施.
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