动物园CNN:使用胸部X射线图进行肺炎分类的零顺序优化卷积神经网络
Saravana Kumar Ganesan1, Parthasarathy Velusamy2, Santhosh Rajendran2
1Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore 641032, India.
Journal of imaging
|January 24, 2025
概括
一个新的深度学习模型,ZooCNN,有效地通过胸部X射线诊断肺炎. 这种自动化系统提供了高精度,帮助医生在资源有限的设置.
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:
- 肺炎是儿童死亡的主要原因,特别是在最不发达国家 (LDCs).
- 目前的诊断依赖于胸部X射线 (CXRs),但最不发达国家的放射科医生短缺阻碍了访问.
- 自动诊断系统对于改善服务不足地区的肺炎检测至关重要.
研究的目的:
- 从CXR图像开发和评估一个用于自动化肺炎分类的深度学习模型.
- 解决传统卷积神经网络 (CNN) 的挑战,如过度装配和高计算成本.
- 创建一个高效和准确的工具来区分正常肺 (NL),细菌性肺炎 (BP) 和病毒性肺炎 (VP).
主要方法:
- 使用零顺序优化 (Zoo) 开发了一个新的零顺序优化卷积神经网络 (ZooCNN).
- 适应合成采样 (ADASYN) 方法用于Kaggle CXR图像数据集上的类平衡.
- 使用ZooPlatform (ZooPT) 进行了超参数微调,导致体重减少72%.
主要成果:
- 动物园CNN模型实现了高性能指标:97.27%的准确性,97.00%的灵敏性,98.60%的特异性,97.03%的F1得分.
- 该模型与当代肺炎分类模型相比,显示出更高的疗效.
- 优化的架构显著降低了模型重量,提高了计算效率.
结论:
- 动物园CNN介绍了一种高度准确和高效的自动化解决方案,用于从CXR图像中分类肺炎.
- 这种模型有潜力显著支持临床决策,特别是在最不发达国家,放射科医生的访问有限.
- 开发的方法为改善儿科肺炎诊断和患者治疗结果提供了一个有希望的工具.
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