转移基于学习的分类器将从医院的数据库中自动提取虚假X射线图像
Manar Abu Talib1, Mohammad Adel Moufti2, Qassim Nasir3
1Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates.
International dental journal
|September 5, 2024
概括
这项研究开发了一种人工智能 (AI) 系统,可以自动区分真实患者的牙科放射和烯酸块上的假训练图像. 人工智能实现了高精度,改善了数字牙科存档中的数据管理.
科学领域:
- 牙科放射学 牙科放射学
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 牙科学生使用烯酸块与提取的牙为临床前放射学培训.
- 牙科记录的数字化导致了训练和患者放射图像的混合存储.
- 手动过这些混合图像是低效和有问题的.
研究的目的:
- 开发一种自动化的方法来区分口内射线图与人工训练图像.
- 提高数字牙科图像存档的准确性和效率.
主要方法:
- 利用人工智能 (AI) 解决方案进行自动图像差异化.
- 应用了转移学习的概念,使用VGG16预先训练的模型.
- 在牙科医院的数据集上训练和测试模型.
主要成果:
- VGG16模型实现了98.8%的准确性,99.2%的F1得分和100%的回忆.
- 这些结果显著超过了最初的基线模型 (96.5%准确率,97.5%F1得分,98.9%回忆).
- 人工智能系统在识别人工图像方面表现出高灵敏度.
结论:
- 基于转移学习的AI系统有效地区分了真实口内放射和人工训练图像.
- 拟议的系统为清洁和管理数字牙科图像档案提供了准确和自动化的解决方案.
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