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通过使用深度学习方法对白细胞进行全面的数据分析,并进行分类和细分

Şeyma Nur Özcan¹, Tansel Uyar¹, Gökay Karayeğen²

¹Biomedical Engineering Department, Başkent University, Ankara, Turkey.

Cytometry. Part A : the journal of the International Society for Analytical Cytology

|April 2, 2024

概括

这项研究结合了多个数据集,以使用深度学习进行准确的白细胞分类和细分. 拟议的方法在独立数据集上实现了高精度,显示了临床诊断的潜力.

科学领域:

医学图像分析医学图像分析
计算生物学计算生物学
人工智能在医学中的应用

背景情况:

深度学习被广泛用于人类外围血液细胞分析.
以前的研究经常单独分析数据集,限制了概括性.
结合用于血细胞分类和细分的多个数据集仍然未得到充分探索.

研究的目的:

开发和评估一个深度学习模型来分类和细分人类外周血细胞.
研究将多个数据集结合起来以提高模型性能的有效性.
为了评估模型的准确性,在不依赖于火车的数据集上进行临床应用.

主要方法:

使用了四个不同的数据集的混合物来对白细胞进行分类.
应用了三个神经网络架构 (CNN,UNet,SegNet) 来进行细分.
提出了一个卷积神经网络 (CNN) 用于核和细胞质检测.

主要成果:

在列车独立数据集上实现了98.03%的均衡分类准确度和97.27%的测试准确度.
拟议的CNN在列车依赖的数据集上达到98.9%的准确性,在列车独立的数据集上达到92.82%的准确性.
从火车独立数据集中检测白细胞的高准确性.

结论:

拟议的深度学习方法有效地使用组合数据集对白细胞进行分类和细分.
该模型在未见的数据上表现出强的性能,表明了稳定性.
这种方法显示出作为临床应用的诊断工具的显著前景.

关键词:

在美国,CNN是CNN.图像分类图像分类图像分类独立的数据集是一个独立的数据集.核和细胞质细分的核和细胞质细分.白细胞是白细胞的组成部分.

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