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使用多重预处理和优化CNN模型的白细胞分类

Oumaima Saidani¹, Muhammad Umer², Nazik Alturki¹

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific reports

|February 12, 2024

概括

这项研究通过使用先进的预处理和数据增强,从显微镜图像中增强了白细胞 (WBC) 的分类. 一种新的深度学习方法达到0.99准确度,超过了免疫细胞分析的现有方法.

科学领域:

血液学血液学血液学
医疗成像医学成像
计算生物学计算生物学

背景情况:

白细胞 (WBC) 对免疫反应至关重要,它们的异常标志着白血病等疾病.
以前的WBC分类研究往往缺乏准确性,原因是特征集有限,并且专注于较少的细胞类型.

研究的目的:

开发一种高精度和计算效率的方法,用微观图像对WBC类型进行分类.
通过改善特征提取和模型性能来解决现有的WBC分类技术的局限性.

主要方法:

采用广泛的预处理和数据增强技术来生成一个强大的功能集.
利用传统的深度学习和转移学习模型来进行WBC图像分类.
将拟议的方法与最先进的机器和深度学习模型进行比较.

主要成果:

拟议的方法使用预处理的特征集与卷积神经网络,实现了0.99.9的分类准确性.
与现有的最先进的方法相比,证明了卓越的性能和计算效率.
成功解决了分类多种WBC类型的高精度挑战.

结论:

这种新的方法显著提高了WBC分类的准确性和效率.
这种方法为血液学中的自动诊断工具提供了有希望的进步.
突出了医疗图像分析中先进特征工程的重要性.

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