深度学习模型的比较,用于在胸部X射线图像中检测COVID-19
Enrique Peláez1, Ricardo Serrano1, Geancarlo Murillo1
1Escuela Superior Politécnica del Litoral - ESPOL University, Electrical and Computer Engineering, Guayaquil, Ecuador.
概括
这项研究比较了COVID-19诊断的四种深度学习模型,达到95%以上的准确性. VGG-19和Inception网络在识别疾病方面表现优异.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由于COVID-19的流行,需要快速的诊断工具.
- 深度学习 (DL) 提供了自动疾病检测的潜力.
研究的目的:
- 设计和比较各种DL架构用于COVID-19诊断.
- 评估转移学习在COVID-19医疗图像分析中的有效性.
主要方法:
- 开发并比较了四个DL模型:CNN,VGG-19,Inception和MobileNet. 这四个模型是:
- 在45个实验中利用转移学习进行模型培训和验证.
- 实施了用于检测疾病存在的二进制分类.
主要成果:
- 这四种DL模型都实现了超过95%的诊断准确度.
- VGG-19和Inception网络配置显示了最高的性能指标.
- 对比分析利用多个评分指标进行了可靠的评估.
结论:
- 深度学习模型,特别是VGG-19和Inception,对于COVID-19诊断非常有效.
- 转移学习提高了DL模型在识别COVID-19中的性能.
- 这项研究证实了人工智能在流行病应对和诊断方面的潜力.
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