人工神经网络和卷积神经网络用于预测牙虫
Katrul Nadia Basri1, Farinawati Yazid2, Mohd Norzaliman Mohd Zain3
1School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia; Photonics Technology Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 23, 2024
概括
机器学习和紫外线光谱学可以检测早期的牙损伤. 人工神经网络 (ANN) 和卷积神经网络 (CNN) 对于早期虫检测和查具有很高的准确性.
科学领域:
- 牙科 牙科是指牙科的专业.
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 牙损是一个普遍存在的全球健康问题,影响儿童和成人.
- 现代牙科强调预防措施,以减少牙的发生率.
- 早期发现牙损伤对于有效的干预至关重要.
研究的目的:
- 研究机器学习算法的有效性与紫外线光谱相结合,用于早期检测牙损伤.
- 根据光谱数据开发和验证可靠的模型,用于根据光谱数据对虫进行分类.
主要方法:
- 利用紫外线光谱学从牙收集光谱数据.
- 应用了预处理技术,包括平均中心 (MC),自动尺度 (AS) 和萨维茨基-戈莱光滑 (SG).
- 采用超参数调节的人工神经网络 (ANN) 和卷积神经网络 (CNN) 根据国际虫检测和评估系统 (ICDAS) 进行数据分类.
主要成果:
- 最好的ANN模型实现了0.85的精度和1.00的精度.
- 结合Savitzky Golay光滑,CNN模型表现出完美的性能,在验证数据上准确度,精度,灵敏度和特异性为1.00.
- 美国广播公司 (ANN) 和美国有线电视新闻网 (CNN) 的两种模型都证明能够对早期虫查进行强有力的分类.
结论:
- 机器学习技术,特别是ANN和CNN,与紫外线光谱学相结合,为早期检测牙损伤提供了一个有希望的方法.
- 这些模型可以作为早期查的有效工具,帮助及时进行预防和治疗干预.
- 该研究强调了光谱分析与先进的算法相结合的潜力,可以改善牙科诊断.
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