SkinDisNet:用于皮肤疾病检测和分类的临床图像和元数据的综合数据集
Marjia Sultana1, Rakib Hossain Sajib1, Iffat Ara Badhan2
1Department of Computer Science and Engineering, Begum Rokeya University, Rangpur, Rangpur 5404, Bangladesh.
Data in brief
|December 1, 2025
概括
这项研究引入了一个新的数据集,包括1710张智能手机图像,用于检测皮肤疾病. 该数据集有助于机器学习更准确地诊断诸如湿疹和等疾病.
科学领域:
- 皮肤病学 皮肤病学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 皮肤疾病给全球健康带来了重大挑战,影响了数百万人.
- 正确的诊断往往是困难的,因为视觉上的相似性和症状的变化.
- 缺乏特定区域的注释数据集阻碍了用于皮肤疾病检测的强大的机器学习模型的开发.
研究的目的:
- 提出一种新的,精心策划的数据集,用于通过机器学习和计算机视觉来推进皮肤疾病检测.
- 为了解决皮肤状况分类的特定区域注释数据的稀缺性.
- 为了促进可重复的研究在自动化皮肤病诊断.
主要方法:
- 在孟加拉国通过智能手机摄像机捕获的1710种皮肤疾病的临床图像的汇编.
- 将图像分为六个不同的类别:无氧性皮肤炎,接触性皮肤炎,湿疹,疹,斑疹性皮肤炎和体内.
- 包括精心策划的元数据,以及每个图像的基本临床和人口统计细节.
主要成果:
- 现在可以获得一个包含1710个标记皮肤疾病图像的综合数据集.
- 数据集分为六个临床相关类别,有助于特定疾病的识别.
- 关联的元数据增强了数据集的实用性,用于详细分析和模型训练.
结论:
- 新提交的数据集将对基于机器学习的皮肤疾病检测研究做出重大贡献.
- 它为开发和验证皮肤病应用的计算机视觉模型提供了宝贵的资源.
- 该数据集旨在提高皮肤病诊断的准确性和可访问性,特别是在代表性不足的地区.
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