A comprehensive dental dataset of six classes for deep learning based object detection study

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2024-09-21 DOI:10.1016/j.dib.2024.110970

Rubaba Binte Rahman, Sharia Arfin Tanim, Nazia Alfaz, Tahmid Enam Shrestha, Md Saef Ullah Miah, M.F. Mridha

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引用次数: 0

Abstract

This article presents a dental dataset for the improvement of research on deep learning-based detection and classification of dental diseases. The dataset is consisted of 232 panoramic dental radiographs, categorized into six major classes: healthy teeth, caries, impacted teeth, infections, fractured teeth, and broken-down crowns/roots (BDC/BDR). The images were collected from three renowned private clinics in Dhaka, Bangladesh, with the help of an experienced dental practitioner who ensured the confidentiality of patients and high-quality data acquisition using a 64-megapixel Android phone camera. To enhance the value of the dataset for machine and deep learning applications, we applied Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image enhancement and augmented the data. The images were annotated using the CVAT tool and reviewed by dental experts. This benchmark dataset is publicly available and provides a valuable resource for researchers in artificial intelligence, computer science, and dental informatics to promote interdisciplinary collaboration and the development of advanced algorithms for dental disease detection.

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基于深度学习的物体检测研究的六类综合牙科数据集

本文介绍了一个牙科数据集，用于改进基于深度学习的牙科疾病检测和分类研究。该数据集由 232 张全景牙科 X 光片组成，分为六大类：健康牙齿、龋齿、阻生牙、感染、牙齿折断和牙冠/牙根折断（BDC/BDR）。这些图像来自孟加拉国达卡的三家知名私人诊所，由一名经验丰富的牙科医生协助收集，他使用 6400 万像素的安卓手机摄像头确保了患者的保密性和高质量的数据采集。为了提高数据集在机器学习和深度学习应用中的价值，我们应用了对比度受限自适应直方图均衡化（CLAHE）技术进行图像增强，并对数据进行了扩增。使用 CVAT 工具对图像进行了注释，并由牙科专家进行了审查。这个基准数据集是公开可用的，为人工智能、计算机科学和牙科信息学研究人员提供了宝贵的资源，促进了跨学科合作和牙科疾病检测先进算法的开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.