推进WBC分类:一个混合ConvNextV2-Swin变压器框架与R3GAN数据平衡和CLAHE预处理
Mohammad Momenian1, Seyed Vahab Shojaedini2
1Department of Computer Engineering, Faculty of Engineering, Azad University, E-Campus, Tehran, Iran. mohammad.momenian@iauec.ac.ir.
Journal of imaging informatics in medicine
|December 2, 2025
概括
这项研究引入了一种用于白细胞分类的新型混合框架,显著提高了像基细胞这样的罕见细胞类型的准确性. 该方法在处理不平衡的数据集方面表现出色,为血液学诊断提供了强大的解决方案.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
- 图像分析 图像分析
背景情况:
- 准确的白细胞 (WBC) 分类对于血液学诊断至关重要.
- 分类罕见细胞类型和处理不平衡的数据集存在重大挑战.
- 现有的方法在数据变化和有限的样本大小方面扎.
研究的目的:
- 为增强WBC分类开发一种新的混合框架.
- 为了应对罕见细胞类型和血液学中不平衡的数据集的挑战.
- 提高自动化WBC分类系统的准确性和效率.
主要方法:
- 一个三组合混合框架,集成ConvNeXtV2-Swin变压器用于特征提取.
- 利用强化可靠强大的生成对抗网络 (R3GAN) 进行智能少数阶级增强.
- 在适应性图像预处理中采用对比度有限的自适应式直方图形平衡 (CLAHE).
主要成果:
- 在具有挑战性的Raabin数据集上实现了99.1%的准确性,比最先进的方法高出2-10%.
- 证明了非常高的数据效率,仅使用50%的培训数据保持94%的准确性.
- 成功地减轻了类不平衡,并保留了生成样本中的生物忠实性.
结论:
- 拟议的框架为WBC分类提供了一个强大而准确的解决方案,特别是在罕见细胞和不平衡数据方面.
- 先进的人工智能技术和预处理的协同集成为临床部署提供了一个范例.
- 该框架的数据效率使其适用于血液诊断中资源有限的环境.
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