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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

1.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.8K

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相关实验视频

Updated: Jun 16, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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一个可解释的基于AI的血细胞分类,使用优化的卷积神经网络进行分类.

Oahidul Islam1, Md Assaduzzaman2, Md Zahid Hasan2

  • 1Dept. of EEE, Daffodil International University, Dhaka, Bangladesh.

Journal of pathology informatics
|August 20, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个增强的卷积神经网络 (CNN),用于准确的白细胞 (WBC) 分类. 开发的模型实现了高精度,帮助医疗专业人员诊断疾病.

关键词:
可解释的人工智能格拉德-卡姆 (GRAD-CAM) 是一个在 LIME 时代,优化了CNN的优化方式.这就是 SHAP SHAP 的意思.转移学习转移学习白细胞是白细胞的组成部分.

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Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
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相关实验视频

Last Updated: Jun 16, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
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科学领域:

  • 医疗成像医学成像
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 准确分类白细胞 (WBCs) 对于疾病诊断至关重要.
  • 现有的方法可能会面临噪音的挑战,并在血液细胞图像中进行特征提取.

研究的目的:

  • 开发一个增强的卷积神经网络 (CNN),用于精确的血液细胞检测和分类.
  • 为了提高医疗应用中CNN模型的可解释性和透明度.

主要方法:

  • 利用图像预处理技术 (填充,值,侵蚀,扩张,掩盖) 来增强血细胞图像.
  • 优化了CNN架构和超参数,以提高性能.
  • 将拟议的模型与转移学习模型进行比较 (Inception V3,MobileNetV2,DenseNet201).
  • 使用SHAP,LIME,Grad-CAM和Grad-CAM++来实现模型的可解释性和可视化.

主要成果:

  • 拟议的CNN模型实现了测试准确率为99.12%,精度为99%,F1得分为99%.
  • 在血液细胞分类方面表现优于Inception V3,MobileNetV2和DenseNet201.
  • 在本地化方面,Grad-CAM++在本地化方面表现略高于Grad-CAM.
  • 可解释性技术为模型决策提供了洞察力.

结论:

  • 增强的CNN为血液细胞分类提供了高度准确和可解释的解决方案.
  • 该模型集成到端到端系统 (web和Android) 中,便于医疗专业人员在实践中使用.
  • 这一进步支持通过自动化细胞分析更可靠的疾病诊断.