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基于深度学习的六个类的综合牙科数据集对象检测研究基于深度学习的六个类
Rubaba Binte Rahman1, Sharia Arfin Tanim1, Nazia Alfaz1
1American International University Bangladesh Kuratoli 408/1, Dhaka, Bangladesh.
Data in brief
|October 14, 2024
概括
一个新的牙科数据集,包括232张全景放射图,有助于深度学习检测牙和感染等疾病. 这种公开可用的资源增强了人工智能驱动的牙科诊断和研究.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 牙科信息学 牙科信息学
- 医疗成像医学成像
背景情况:
- 深度学习模型需要大型,多样化的数据集来准确检测牙科疾病.
- 现有的牙科数据集可能缺乏足够多样性或高分辨率成像,以满足先进的人工智能应用.
- 公共可访问的注释数据集对于在医疗保健中推进人工智能至关重要.
研究的目的:
- 为人工智能研究引入一种全新的,全面的牙科放射数据集.
- 促进深度学习算法的开发和基准测试,用于牙科疾病的分类.
- 促进人工智能研究人员和牙科专业人员之间的跨学科合作.
主要方法:
- 从孟加拉国达卡的三家诊所收集了232张全景牙科放射图.
- 使用对比度有限的自适应直方体平衡 (CLAHE) 和数据增强,提高图像质量.
- 使用CVAT工具的注释图像,经过专家牙科审查以确保准确性.
主要成果:
- 数据集包括六个类别:健康的牙,,受损的牙,感染,骨折的牙和破碎的冠状/根 (BDC/BDR).
- 该数据集是公开可用的,支持人工智能驱动的牙科诊断研究.
- 使用64兆像素手机摄像头获得的高质量图像确保了数据实用性.
结论:
- 这一基准数据集是推动牙科人工智能发展的宝贵资源.
- 该数据集将加速深度学习模型的开发,用于检测和分类各种牙科疾病.
- 促进牙科信息学和人工智能领域的未来研究和跨学科合作.
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