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可解释的COVID-19检测基于使用端到端RegNet架构的胸部X射线

Mohamed Chetoui¹, Moulay A Akhloufi¹, El Mostafa Bouattane²

¹Perception, Robotics, and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada.

|June 28, 2023

概括

在胸部X射线上使用RegNetX032的深度学习模型准确检测COVID-19. 这种人工智能工具显示出高灵敏度和特异性,有助于快速诊断和患者管理.

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:

由于COVID-19的流行,需要快速准确的诊断工具.
胸部X射线 (CXR) 对于评估呼吸系统状况至关重要.
早期识别COVID-19对于控制其传播至关重要.

研究的目的:

用CXR图像验证和测试用于COVID-19检测的深度学习模型.
为此诊断任务调整和训练RegNetX032卷积神经网络 (CNN).
根据RT-PCR参考标准评估模型的性能.

主要方法:

一个深度卷积神经网络 (CNN) 模型,RegNetX032,被定制并训练在来自五个数据集的15,000多张CXR图像上.
该模型在蒙特福医院的一个独立数据集上进行了测试.
使用包括曲线下面面积 (AUC),灵敏度和特异性在内的指标来评估性能,并使用多二进制分类.

主要成果:

微调的RegNetX032模型实现了96.0%的准确性和99.1%的AUC用于COVID-19检测,具有98.0%的灵敏度和93.0%的特异性.
在将COVID-19与肺炎相比正常分类时,该模型实现了99.1%的AUC,96.0%的灵敏度和93.0%的特异性.
验证集显示高平均精度 (98.6%) 和AUC (98.0%),显示出强大的概括性.

结论:

深度学习模型展示了通过胸部X射线检测COVID-19的出色性能和概括性.
这种人工智能工具可以自动检测COVID-19,协助患者分拣和隔离决策.
该模型作为放射科医生和临床医生在诊断COVID-19时的有价值的辅助工具.

关键词:

在 COVID-19 疫情中,RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet RegNet Regnet Regnet Regnet Regnet Regnet Regnet Regnet Regnet Regnet Regnet 卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.

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