卷积神经网络 (CNN) 用于在儿科胸部放射图上对肺炎进行分类
Yash S Saboo1, Saarthak Kapse2, Prateek Prasanna2
1Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, USA.
Cureus
|September 27, 2023
概括
优化人工智能 (AI) 模型在胸部放射图 (CXRs) 上进行儿科肺炎检测至关重要. 这项研究发现,具有特定超参数的VGG-19获得了最高的准确性,推低脱学率和Adam或RmsProp优化器.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 肺炎对具有发展免疫系统的幼儿构成重大风险.
- 放射科医生在胸部放射图 (CXR) 上诊断儿科肺炎可能是主观的,可能缺少微妙的发现.
- 人工智能 (AI) 和卷积神经网络 (CNN) 提供客观和精确的诊断支持.
研究的目的:
- 为了确定最佳的CNN架构和超参数组合,以准确地检测儿科肺炎.
- 使用人工智能提高肺炎诊断的客观性和准确性.
主要方法:
- 通过使用五个CNN (VGG 16,VGG 19,DenseNet 121,DenseNet 169,InceptionResNet V2) 评估了60个模型,其中有12个超参数组合.
- 在未识别的CXR数据集上测试了优化器 (Adam,RmsProp,SGD),批量大小 (32,64) 和学率 (0.5,0.7).
- 最初在ImageNet上训练CNN,然后在CXR数据集上进行微调 (70%的训练,20%的验证,10%的测试).
主要成果:
- VGG-19的丢弃率为0.5,批量大小为32,Adam优化器实现了最高的准确性 (87.9%).
- 与0.7.7相比,0.5的失业率始终提高了准确性,AUROC和AUPRC.
- VGG-19,InceptionResNet V2,DenseNet 169和VGG 16的表现优于DenseNet121;Adam和RmsProp比SGD改进了AUROC/AUPRC;批量大小没有显著的影响.
结论:
- 为肺炎检测CNN推低失学率 (0.5) 和Adam或RmsProp优化器.
- 如果有其他CNN可用,建议不要使用DenseNet121.
- 可以根据计算资源选择批量大小,而不会显著影响性能.
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