基于学习的联合CNN模型用于正统牙骨分类和诊断
Demet Süer Tümen1, Mehmet Nergiz2
1Department of Orthodontics, Faculty of Dentistry, Dicle University, 21280 Diyarbakır, Türkiye.
Diagnostics (Basel, Switzerland)
|April 12, 2025
概括
联合学习 (FL) 与卷积神经网络 (CNN) 显示为正统骨分类的承诺,提高准确性,同时保持数据隐私. 这种人工智能方法使牙科诊所之间的安全合作成为可能.
科学领域:
- 人工智能在牙科中的应用
- 医学图像分析 医学图像分析
- 机器学习用于正牙科.
背景情况:
- 准确的骨分类对于正牙诊断和治疗规划至关重要.
- 目前的方法可能缺乏效率或损害患者数据隐私.
- 联合学习 (FL) 为合作模式培训提供了一个潜在的解决方案,而不需要集中敏感数据.
研究的目的:
- 评估联合卷积神经网络 (CNN) 模型在正统牙骨分类中的有效性.
- 将FL的表现与集中学习 (CL) 和本地学习 (LL) 框架进行比较.
- 评估FL保持数据隐私的能力,同时实现协作模式培训.
主要方法:
- 使用了DenseNet121 CNN架构,增强了注意力机制和聚合块.
- 从ISBI和Dicle数据集中对脑表图像进行训练和评估的模型.
- 在CL,LL和FL框架中使用准确度,灵敏度和特异性的基准模型性能.
主要成果:
- 与基线模型相比,联合CNN模型实现了超过26%的精度改进.
- 增强的DenseNet121模型在FL下显示出显著的性能增长,与CL相比.
- 具体模型显示了显著的改进,例如,在ISBI数据集上,比LL获得20.86%的收益.
结论:
- 联合CNN模型显示了 ортодонтика骨分类的巨大潜力.
- 在保持数据隐私的同时,FL可实现增强的协作模式培训.
- 这种方法通过跨机构的安全,协作式人工智能推进了正牙诊断.
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