基于深度学习的系统结合了胸部X射线和计算机断层扫描图像,用于COVID-19诊断
Hui Ding1, Lingyan Fan2, Jingfeng Zhang1
1Department of Radiology, Ningbo No.2 Hospital, Ningbo, Zhejiang, China.
British journal of hospital medicine (London, England : 2005)
|August 30, 2024
概括
集胸部X射线 (CXR) 和计算机断层扫描 (CT) 扫描的深度学习模型显著提高了2019年新冠肺炎 (COVID-19) 诊断的准确性. EfficientNet模型在从医学图像中检测COVID-19方面表现出卓越的表现.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 诊断技术 诊断技术 诊断技术
背景情况:
- 由于COVID-19的流行,人们需要快速准确的诊断工具.
- 传统的诊断方法在速度和准确性方面面临挑战.
- 整合多种成像模式对于增强疾病检测至关重要.
研究的目的:
- 通过整合胸部X射线 (CXR) 和计算机断层扫描 (CT) 图像来提高COVID-19检测的准确性.
- 开发和评估深度学习模型,以改善COVID-19诊断.
- 为了比较不同深度学习架构在分析COVID-19医疗图像方面的性能.
主要方法:
- 使用公开可用的CXR和CT图像数据集 (COVID-QU-Ex,iCTCF).
- 使用卷积神经网络 (CNN),特别是EfficientNet和ResNet架构,用于图像分类.
- 应用数据预处理,增强和梯度加权类激活映射 (Grad-CAM) 进行解释性.
主要成果:
- 基于EfficientNet的模型比ResNet模型取得了更高的性能.
- 记录了高准确率:使用EfficientNetB5.5的CXR为99.44%和CT为99.81%.
- 从统计学上显著的结果 (p < 0.05) 表明发现的可靠性.
结论:
- 将CXR和CT图像与深度学习相结合,显著提高了COVID-19诊断的准确性.
- 效率网模型表现出卓越的特征提取,表现优于ResNet.
- Grad-CAM为模型决策提供了宝贵的见解,有助于减少错误和加速诊断,以获得更好的患者护理.
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