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血细胞图像细分和分类:系统性审查

Muhammad Shahzad¹, Farman Ali², Syed Hamad Shirazi¹

¹Department of Computer Science and Information Technology, Hazara University, Mansehra, Pakistan.

PeerJ. Computer science

|March 4, 2024

概括

这项调查回顾了血液细胞分析的深度学习,强调了细分,分类和特征选择方法,用于诊断白血病和贫血等疾病. 调查结果显示,手动图像采集和形态特征的趋势,表明需要标准化数据集.

科学领域:

医学图像分析医学图像分析
计算病理学计算病理学
血液学血液学血液学

背景情况:

血液疾病 (白血病,贫血,淋巴瘤,血症) 涉及白细胞 (WBC) 和红细胞 (RBC) 的异常.
准确的诊断依赖于血液学家的专业知识,对计算机辅助诊断 (CAD) 技术越来越感兴趣.
现有的调查缺乏整体观点,只关注细分或分类等特定方面.

研究的目的:

通过深度学习提供血液图像分析的全面和系统审查.
专注于医疗图像处理和WBC和RBC形态表征的深度学习.
涵盖细分,分类,特征选择,评估参数和数据集选择.

主要方法:

在血液图像分析中对深度学习技术的系统文献综述.
对细分,分类,特征选择,评估矩阵和数据集选择方法的分析.
专注于白细胞和红细胞的形态特征.

主要成果:

研究人员经常手动获取图像 (50%用于WBC细分,60%用于RBC细分).
在WBC分类中,ALL-IDB数据集是常见的 (45%),而RBC分类经常使用手工获取的图像 (73%).
形态特征是对分类的首选 (55%为白细胞,80%为红细胞).

结论:

CAD技术,特别是深度学习,可以提高血液疾病的诊断.
不一致的数据集选择凸显了对标准化,高质量的数据集的需求.
未来的研究应该探索和创新形态特征和标准化数据集.

关键词:

分类分类分类分类.形态特征形态特征形态特征红细胞是血液中的红细胞.分段化分段化分段化分段化白细胞是白细胞的组成部分.

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