从CT扫描和X射线图像使用深度学习来检测COVID-19和肺炎,并从CT扫描和X射线图像上部署网络
Nahid Islam1, Abu S M Mohsin1, Shadab Hafiz Choudhury1
1Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh.
PloS one
|July 8, 2024
概括
这项研究引入了一个深度学习框架,用于快速准确地检测COVID-19和肺炎,使用CT扫描和X射线. 人工智能工具的准确性高达99%,为疾病管理提供了具有成本效益的解决方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 肺炎是COVID-19大流行期间呼吸衰竭的主要原因,可能源于各种细菌和病毒病原体,包括SARS-CoV-2.
- 目前COVID-19的诊断方法通常是缓慢的,昂贵的,缺乏足够的准确性和精确性.
- COVID-19的多样性和有时微妙的症状需要先进的检测技术.
研究的目的:
- 开发和评估用于自动检测COVID-19和肺炎的深度学习框架.
- 为了比较著名的深度学习模型 (VGG-19,ResNet-50,Inception V3,Xception) 在疾病识别方面的性能.
- 创建可部署的人工智能工具,用于更快,更具成本效益的诊断.
主要方法:
- 使用了四种深度学习模型:VGG-19,ResNet-50,Inception V3和Xception. 在这个模型中,
- 在CT扫描和X射线图像的单独数据集上训练和测试模型用于COVID-19检测.
- 使用额外的X射线数据集验证了细菌和病毒性肺炎的发现,并实施了Flask应用程序用于可视化.
主要成果:
- 根据具体的模型和使用的数据集,实现了高检测准确度,从86%到99%.
- 确定了最佳的深度学习模型,以在医学图像中区分COVID-19与其他疾病.
- 展示了可部署人工智能框架的可行性,用于实时诊断支持.
结论:
- 深度学习模型显示,从医学成像中准确有效地检测COVID-19和肺炎具有显著的前景.
- 开发的框架可以帮助更快地诊断和更好地管理呼吸道感染.
- 一个人工智能驱动的自动化工具为传统诊断方法提供了经济高效的替代方案.
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