一个基于自我注意的深度学习框架,用于准确有效地检测OPGX光学成像中的牙科疾病
Ramasubramanian Bhoopalan1, S Mirdula1, Priyadharshini Kannusamy1
1Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India.
Scientific reports
|January 21, 2026
概括
这项研究引入了使用牙X射线诊断口腔疾病的深度学习. 视觉变压器 (ViT) 实现了96%的准确性,优于Swin变压器,提供更可靠的,自动化的牙科诊断.
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:
- 口腔疾病越来越令人担忧,需要准确高效的诊断工具.
- 当前的诊断方法可能容易出现人为错误,需要大量的临床医生时间.
- 深度学习的进步为牙科的自动诊断系统提供了潜力.
研究的目的:
- 提出和比较深度学习模型,特别是视觉转换器 (ViT) 和Swin转换器,用于使用Orthopantomogram (OPG) 图像自动诊断牙疾病.
- 评估ViT和Swin Transformer在口腔疾病检测的准确性,精度和回忆方面的性能.
- 评估牙科成像中基于变压器架构的效率和可扩展性.
主要方法:
- 利用了深度学习技术,专注于基于变压器的架构:视觉变压器 (ViT) 和Swin变压器.
- 训练并评估了用于诊断牙疾病的Orthopantomogram (OPG) 图像上的模型.
- 在ViT和Swin Transformer之间比较了性能指标,包括测试准确度,精度和回忆.
主要成果:
- 视觉变压器 (ViT) 的测试准确率为96%,精度为95.8%,回忆率为96.2%.
- 斯温变压器展示了95.2%的竞争精度,具有高效的推断时间和可扩展的复杂性.
- 与Swin Transformer相比,ViT在诊断口腔疾病方面表现优越.
结论:
- 基于变压器的架构,特别是ViT,对于牙科成像中的自动诊断非常有效.
- 拟议的方法为临床医生提供了一个有前途的解决方案,提高了口腔疾病的诊断准确度和缩短诊断时间.
- 深度学习模型可以显著改善口腔疾病的检测,解决对先进诊断系统日益增长的需求.
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