挤压和激发增强卷积神经网络用于多类肺炎分类的胸部X射线图
Kian A Huang1, Haris K Choudhary1, Ashley Santiago1
1Radiology, University of South Florida Morsani College of Medicine, Tampa, USA.
Cureus
|January 20, 2026
概括
两个深度学习模型ResNet50V2-SE和InceptionV3-SE准确地分类了正常,细菌性肺炎,病毒性肺炎和COVID-19检测的胸部X射线. 两种模型都表现出高性能,表明它们作为诊断决策支持工具的潜力.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 放射学中的深度学习
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胸部X射线的准确分类对于诊断各种呼吸系统疾病至关重要,包括不同类型的肺炎和COVID-19.
- 传统的诊断方法可能会耗时,并且可能会受到观察者之间的变化.
- 深度学习模型,特别是卷积神经网络 (CNN),显示出对医疗图像分析的自动化和改进准确性的承诺.
研究的目的:
- 为了比较两个SE增强的CNN架构ResNet50V2-SE和InceptionV3-SE的性能,用于胸部X射线的自动分类.
- 评估模型在正常放射图,细菌性肺炎,病毒性肺炎和COVID-19之间区分能力.
- 评估注意力增强型深度学习模型在放射性诊断中的通用性和诊断潜力.
主要方法:
- 采用了9208张后前胸部X射线图的数据集.
- 采用了两种CNN架构:ResNet50V2和InceptionV3,两者都增强了挤压激发 (SE) 注意力机制.
- 在不同的数据集上训练并验证模型,应用相同的预处理和微调技术.
主要成果:
- ResNet50V2-SE获得了98.18%的测试准确率和AUC为0.9951;InceptionV3-SE获得了97.86%的准确率和AUC为0.9949.
- 两款车型都表现出高级别的性能,F1分数在所有类别中从0.95到1.00不等.
- 麦克纳马的测试显示,两种模型之间的分类性能没有统计学上显著的差异 (p>0.05).
结论:
- 经SE增强的ResNet50V2和InceptionV3 CNN非常准确,可以将胸部X射线分类为正常,细菌性肺炎,病毒性肺炎和COVID-19类别.
- 这些注意力增强的深度学习模型显示出作为放射科医生的有效决策支持工具的巨大潜力.
- 建议对更大,更多样化的临床数据集进行进一步验证,以确认这些先进的诊断工具的临床实用性.
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