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

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 .