使用混合CNN-RNN架构进行COVID-19检测,并从X射线转移学习
Deepti Deshwal1, Pardeep Sangwan1, Naveen Dahiya2
1Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India.
Current medical imaging
|August 18, 2023
概括
本研究介绍了一种混合卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型,用于使用X射线图像准确检测COVID-19. VGG19-RNN架构实现了99%的准确性,有助于及时诊断和疫情控制.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由于COVID-19的全球快速传播,需要有效的诊断工具.
- 准确识别感染者对于控制病毒传播至关重要.
研究的目的:
- 开发一种混合深度学习模型,将CNN和RNN结合起来,以从X射线图像中增强COVID-19检测.
- 利用转移学习来提高诊断准确度.
主要方法:
- 使用四个预训练的CNN (InceptionnetV3,Densenet121,Inception-ResNet V2,VGG19) 来进行特征提取.
- 使用RNN来捕获提取特征中的时间依赖.
- 在包括COVID-19,肺炎和健康X射线在内的多种数据集上评估性能.
- 应用Grad-CAM用于X射线图像中可视化感染区域.
主要成果:
- 混合CNN-RNN架构展示了高准确度,精度,回忆,AUC和F1分数.
- VGG19-RNN模型在COVID-19检测中表现优于其他最先进的方法.
- 在VGG19-RNN方面,实现了最佳的培训和验证准确率,分别为99%和97.70%.
结论:
- 混合CNN-RNN模型有效地捕获空间和时间信息,以改进COVID-19检测.
- 这种方法为从X射线图像中诊断COVID-19提供了强大而高效的解决方案.
- 该模型可以支持医疗保健专业人员进行及时和准确的诊断,帮助全球流行病控制工作.
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