用人工智能增强放射性诊断,用于结核病和COVID-19疾病检测:深度学习检测胸部X射线图
Manjur Kolhar1, Ahmed M Al Rajeh2, Raisa Nazir Ahmed Kazi2
1Department Health Informatics, College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一个深度学习网络,用于从X射线图像中识别肺炎,COVID-19和结核病. 与VGG16.16相比,ResNet50模型在检测这些肺部疾病方面表现出优异的性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胸部X射线对于诊断肺部疾病如肺炎,COVID-19和结核病至关重要.
- 准确及时诊断对于有效的患者治疗和疾病管理至关重要.
- 先进的深度学习模型有可能提高基于图像的诊断的准确性.
研究的目的:
- 开发和评估深度学习网络,以使用胸部X射线图像检测肺炎,COVID-19和结核.
- 为了比较ResNet50和VGG16深度学习模型在识别这些肺部疾病方面的表现.
- 评估数据增强对模型性能的影响.
主要方法:
- 使用胸部X射线图像进行训练和测试深度学习模型,特别是VGG16和ResNet50.
- 采用数据增强技术来增强图像数据集,以改善模型训练.
- 在训练,验证和测试数据集上使用精度,回忆,F1分数和ROC AUC评估模型性能.
主要成果:
- 在准确性和弹性方面,ResNet50模型显著超过VGG16,显示出优异的ROC AUC值.
- 在测试组的所有条件下,ResNet50实现了接近0.99的精度和回忆.
- VGG16也表现出强的表现,结核病检测的精度为0.99和回忆率为0.93.
结论:
- 深度学习模型,特别是ResNet50,显示出从X射线图像中准确可靠地检测肺部疾病的显著前景.
- 开发的模型可以提高COVID-19和结核病等疾病的诊断准确性,有可能改变临床诊断.
- 数据增强和模型选择是推动医学成像分析深度学习应用的关键因素.
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