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Classification of Leukocytes

Classification of Leukocytes

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...

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Updated: Jul 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

高精度和轻量级图像分类网络,以优化淋巴细胞白血病诊断.

Liye Mei^1,2, Chentao Lian¹, Suyang Han³

¹School of Computer Science, Hubei University of Technology, Wuhan, China.

Microscopy research and technique

|October 21, 2024

概括

这项研究引入了一种轻量级的深度学习模型,用于使用骨髓细胞图像快速检测白血病. 该模型达到92.51%的准确性,有助于早期诊断和治疗淋巴细胞白血病.

关键词:

急性淋巴细胞白血病急性淋巴细胞白血病慢性淋巴细胞白血病慢性淋巴细胞白血病卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.轻量级的轻量级的轻量级的轻量级的

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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相关实验视频

Last Updated: Jul 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Published on: November 19, 2018

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科学领域:

血液学血液学血液学
在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

白血病是一种严重的血液癌症,影响免疫系统.
早期检测对于有效的癌症管理和治疗至关重要.
深度学习显示出血液疾病检测的潜力,但面临硬件限制.

研究的目的:

开发一种轻量级的深度学习模型,以高效准确地检测白血病.
解决现有的深度学习模型在数据集大小和设备限制方面的局限性.
为了提高从骨髓细胞图像诊断各种类型的白血病的速度和准确性.

主要方法:

收集了一组高质量的数据集,包括来自85名患有淋巴增殖性瘤的患者的17826张骨髓细胞图像.
使用渐进缩小方法,结合多维修剪 (宽度,深度,分辨率,内核大小).
训练了一种轻量级的深度学习模型,只有640万个参数.

主要成果:

实现了急性淋巴细胞白血病,慢性淋巴细胞白血病和其他骨髓细胞类型的快速鉴定.
在白血病识别方面达到92.51%的准确性.
展示了每秒111个幻灯片的高吞吐量.

结论:

这种轻量级的模型对白血病的诊断有着显著的贡献,尤其是淋巴系统疾病.
该模型有可能提高医学专家在诊断淋巴细胞白血病方面的效率和准确性.
这种方法促进了快速准确的识别,支持及时的医疗干预.