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阿尼RBC数据集:使用RBC图像进行计算机辅助贫血诊断的基准数据集.

Muhammad Shahzad1,2, Syed Hamad Shirazi1, Muhammad Yaqoob3

  • 1Department of Information Technology, Hazara University Mansehra, Dhodial, Mansehra, Khyber Pakhtunkhwa 21120, Pakistan.

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|December 25, 2024
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概括

一个新的贫血红细胞 (AneRBC) 数据集通过分析红细胞形态来帮助人工智能诊断贫血. 这一基准数据集可以更好地培训和测试用于检测贫血的深度学习模型.

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科学领域:

  • 医学图像分析 医学图像分析
  • 血液学 血液学 血液学
  • 人工智能的人工智能

背景情况:

  • 诊断贫血依赖于对外围血液涂抹中的红细胞 (RBC) 形态的视觉分析.
  • 缺乏全面的贫血红细胞数据集阻碍了深层卷积神经网络 (CNN) 的开发,用于自动化红细胞形态分析.

研究的目的:

  • 引入一个基准数据集,贫血RBC (AneRBC),以促进CNN模型的培训和验证,用于贫血中RBC形态分析.

主要方法:

  • 创建的AneRBC数据集有两个版本:AneRBC-I (1000个高分辨率图像与地面真相) 和AneRBC-II (12,000个子图像).
  • 图像包括健康和贫血的红细胞与详细的注释,完整的血清和形态学报告.
  • 用四种最先进的CNN模型进行细分和分类,以验证数据集的实用性.

主要成果:

  • 阿尼RBC-I包含500张健康图像和500张贫血图像,详细介绍了大约155万个红细胞元素.
  • 阿尼RBC-II包含12,000张图像,通过将高分辨率图像分为子图像来从阿尼RBC-I中获得.
  • 该数据集有助于对临床数据进行CNN模型性能验证.

结论:

  • 该AneRBC数据集解决了在开发AI用于贫血诊断时对标准化数据的关键需求.
  • 本资源支持计算机辅助诊断红细胞形态形的进展.
  • 该数据集可以对CNN进行强有力的培训和测试,以改善贫血检测.