通过转移学习和深度学习改善胸部X射线图像中的肺结核检测:卷积神经网络架构的比较研究
Alex Mirugwe1, Lillian Tamale2, Juwa Nyirenda3
1School of Public Health, Makerere University, Kawalya Kaggwa Close, Plot 20A, Kampala, Uganda, 256 701120534.
JMIRx med
|July 1, 2025
概括
更简单的卷积神经网络模型,如VGG16,在通过胸部X射线检测结核病 (TB) 中显示出高精度. 这些模型提供有效的结核病诊断,而不需要复杂的数据增强.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 结核病 (TB) 构成了重大的全球卫生挑战,目前的诊断往往是资源密集型和难以获得的.
- 对于早期发现结核病和改善治疗结果的有效和准确的诊断方法有着至关重要的需求.
研究的目的:
- 评估六个卷积神经网络 (CNN) 架构,用于将胸部X射线图像 (CXR) 分类为正常或结核病阳性.
- 评估数据增强对模型性能,训练时间和参数数量的影响.
主要方法:
- 在4200张CXR图像 (700张结核阳性,3500张正常) 的数据集上训练并测试了六个CNN (VGG16,VGG19,ResNet50,ResNet101,ResNet152,Inception-ResNet-V2).
- 评估模型使用准确度,精度,回忆,F1得分和AUC.
- 通过比较训练时间和参数数量来分析计算效率.
主要成果:
- VGG16实现了最高的性能,准确率为99.4%,精度为97.9%,回忆率为98.6%,F1得分为98.3%,AUC为98.25%.
- 像VGG16这样的更简单的模型以更少的计算资源显示出更高的诊断准确性.
- 数据增强并没有提高性能,更大的模型 (ResNet152,Inception-ResNet-V2) 需要更长的训练时间,但没有相应的收益.
结论:
- VGG16为CXR图像中的结核病检测提供了诊断准确性和计算效率的最佳平衡.
- 模型的选择应根据特定任务的要求进行,以实现有效的医学图像分类.
- 研究结果为未来的研究和在结核病诊断中AI的临床实施提供了宝贵的见解.
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