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...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 4; pp. 3315 - 3330 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196241812&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196241812 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2026 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196241812 189902875 196241812 196241812 10.1007/s10278-025-01740-y 196241812 ppf: 3315 ppct: 15 formats: tig: atl: Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing. aug: au: Momenian, Mohammad Shojaedini, Seyed Vahab affil: https://ror.org/01kzn7k21 Department of Computer Engineering, Faculty of Engineering, Azad University, E-Campus, Tehran, Iran sug: subj: Conceptual Framework Microscopy Methods Leukocytes Deep Learning Generative Adversarial Networks Image Processing, Computer Assisted Leukocyte Count Convolutional Neural Networks Autoanalysis Human Algorithms Neural Networks (Computer) Sensitivity and Specificity Resource-Limited Settings Basophils Neutrophils Artificial Intelligence Medical Informatics Automation Image Interpretation, Computer Assisted Cytology Image Enhancement ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|