对深度学习模型进行比较评估,以对全景射线图上的受影响的上犬类进行分类
Nazlı Tokatlı1, Buket Erdem2, Mustafa Özcan2
1Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istanbul Health and Technology University, 34275 Istanbul, Turkey.
这项研究引入了一种深度学习模型,用于在X射线上识别受影响的上犬. VGG16模型实现了高精度,显示了改善牙科诊断的潜力.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 准确识别受影响的上牙对牙科治疗计划至关重要.
- 手动解读X射线图是耗时且变化的.
- 深度学习为自动化分析提供了一个潜在的解决方案.
研究的目的:
- 开发和评估深度学习方法,用于自动分类受影响的上犬.
- 为了比较不同卷积神经网络 (CNN) 架构的性能.
主要方法:
- 一项回顾性研究使用694张注释的全景射线图.
- 转移学习被应用于四个预先训练有素的CNN:ResNet50,Xception,InceptionV3和VGG16.
- 模型使用准确性,精度,回忆,特异性和F1分数进行评估.
主要成果:
- VGG16以99.28%的准确度和99.43%的F1分数获得了最高的性能.
- 为了潜在的临床用途,创建了一个原型诊断接口.
- 这些模型在对受影响的上犬进行分类方面表现出有效性.
结论:
- 深度学习模型,特别是VGG16,显示出增强牙科诊断工作流程的前景.
- 需要进一步的多中心验证,以确认在临床环境中的通用性.
- 自动分类可以提高识别受影响牙的效率和准确性.
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