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<p>White blood cell (WBC) classification remains a critical challenge in hematological diagnostics, particularly for rare cell types such as basophils and imbalanced datasets. This study introduces a novel three-component hybrid framework that synergistically integrates: (1) ConvNeXtV2-Swin Transformer for dual-scale hierarchical feature extraction—combining ConvNeXtV2’s depthwise convolutions with Swin Transformer’s shifted window attention to capture both local cellular morphology and global contextual dependencies; (2) R3GAN (Reinforced Reliable Robust Generative Adversarial Network) for intelligent minority class augmentation through reinforcement learning-guided sample generation, effectively mitigating class imbalance while preserving biological fidelity; and (3) CLAHE (Contrast-Limited Adaptive Histogram Equalization) for adaptive preprocessing to normalize imaging variations. Evaluated on the challenging Raabin dataset—characterized by severe class imbalance (301 basophils vs. 8887 neutrophils) and limited diversity—the proposed architecture achieves 99.1% accuracy, surpassing state-of-the-art methods by 2–10%. Notably, the framework demonstrates exceptional data efficiency, maintaining 94% accuracy with only 50% training data. The synergistic integration of architectural innovation, intelligent data synthesis, and adaptive preprocessing establishes a robust paradigm for clinical deployment in resource-constrained environments. Source code is publicly available at <a href="https://github.com/momenianmohammad/wbc-convnextv2swin-r3gan-eccgan">https://github.com/momenianmohammad/wbc-convnextv2swin-r3gan-eccgan</a>.</p>

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Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • Mohammad Momenian,
  • Seyed Vahab Shojaedini

摘要

White blood cell (WBC) classification remains a critical challenge in hematological diagnostics, particularly for rare cell types such as basophils and imbalanced datasets. This study introduces a novel three-component hybrid framework that synergistically integrates: (1) ConvNeXtV2-Swin Transformer for dual-scale hierarchical feature extraction—combining ConvNeXtV2’s depthwise convolutions with Swin Transformer’s shifted window attention to capture both local cellular morphology and global contextual dependencies; (2) R3GAN (Reinforced Reliable Robust Generative Adversarial Network) for intelligent minority class augmentation through reinforcement learning-guided sample generation, effectively mitigating class imbalance while preserving biological fidelity; and (3) CLAHE (Contrast-Limited Adaptive Histogram Equalization) for adaptive preprocessing to normalize imaging variations. Evaluated on the challenging Raabin dataset—characterized by severe class imbalance (301 basophils vs. 8887 neutrophils) and limited diversity—the proposed architecture achieves 99.1% accuracy, surpassing state-of-the-art methods by 2–10%. Notably, the framework demonstrates exceptional data efficiency, maintaining 94% accuracy with only 50% training data. The synergistic integration of architectural innovation, intelligent data synthesis, and adaptive preprocessing establishes a robust paradigm for clinical deployment in resource-constrained environments. Source code is publicly available at https://github.com/momenianmohammad/wbc-convnextv2swin-r3gan-eccgan.