深度学习中的多模式特征融合,用于全面的牙状况分类
Shang-Ting Hsieh1, Ya-Ai Cheng2
1Department of Health Beauty, Fooyin University, Kaohsiung City, Taiwan.
Journal of X-ray science and technology
|January 13, 2024
概括
使用深度学习和特征融合的自动牙科状况分类显著提高了诊断准确度. 将支持矢量机与合特征相结合,实现了高性能,帮助牙科专业人员.
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
- 机器学习用于医疗保健
背景情况:
- 越来越多的牙健康问题需要准确和及时的诊断.
- 自动牙科条件分类系统为支持临床需求提供了潜在的解决方案.
研究的目的:
- 评估深度学习方法和多式联络功能融合,用于自动化牙状况分类.
- 提高牙科诊断工具的精度和稳定性.
主要方法:
- 使用了11653张牙科图像的数据集,涉及6种疾病:,结石,牙炎,牙变色,和牙下垂.
- 使用五个卷积神经网络 (CNN) 模型提取特征并将它们合并.
- 开发了使用支持矢量机 (SVM) 和天真贝叶斯分类器的分类模型.
主要成果:
- 具有特征融合的SVM分类器实现了0.909的卡帕指数和0.925.9的准确性.
- 这种表现明显超过了个别的CNN模型,例如EfficientNetB0 (卡帕:0.814,准确率:0.847).
结论:
- 功能融合与先进的机器学习算法相结合,提高了牙科分类的精度和稳定性.
- 这种方法是牙科专业人员的宝贵工具,提高了诊断准确性和患者的结果.
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