基于X射线图像的COVID-19检测使用深度学习
Aleka Melese Ayalew1, Ayodeji Olalekan Salau2,3, Yibeltal Tamyalew4
1Department of Information Technology, University of Gondar, Gondar, Ethiopia.
Multimedia tools and applications
|June 26, 2023
概括
这项研究开发了一个卷积神经网络 (CNN) 模型,通过胸部X射线检测COVID-19,实现更快的诊断和患者查的高准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- COVID-19是一种呼吸道感染,主要影响肺部.
- 胸部X射线对于诊断和管理COVID-19至关重要.
- 准确和快速的检测方法对于患者护理至关重要.
研究的目的:
- 开发和评估一个卷积神经网络 (CNN) 模型,用于从胸部X射线图像中检测COVID-19.
- 为了将X射线图像分类为正常或COVID-19阳性.
- 评估深度学习在放射图像中识别COVID-19指标方面的有效性.
主要方法:
- 使用一个卷积神经网络 (CNN) 架构与ReLU和西格状激活功能.
- 实施图像处理技术,包括增强,细分和裁剪.
- 使用Python和OpenCV进行模型开发和分类,并使用支持矢量机 (SVM)进行最终分类.
主要成果:
- 美国有线电视新闻网 (CNN) 模型在对胸部X射线进行分类方面表现出了很高的性能.
- 通过优化图像处理技术,实现了99.8%的训练精度和99.1%的测试精度.
- 该模型提取的深度特征证明在COVID-19检测中是一致且可靠.
结论:
- 拟议的基于CNN的方法提供了一种有效和准确的方法,用于通过胸部X射线检测COVID-19.
- 该模型有助于更快地诊断和查COVID-19患者,帮助放射科医生.
- 深度学习模型显示了医疗图像分析在传染病识别方面的巨大潜力.
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