A Hybrid ViT-ECA-CF Framework with Adaptive Loss Reweighting for Imbalanced White Blood Cell Classification

Abstract

Background: White blood cells (WBCs), also known as leukocytes, play a central role in immune defense, and accurate WBC classification is essential for a hematological assessment and computer-assisted diagnosis. However, a severe class imbalance remains a major challenge in deep-learning-based WBC classification, particularly when minority classes contain substantially fewer training samples. Although data augmentation is commonly used to address this issue, synthetic transformations may fail to capture the complex morphological and appearance variations of WBCs and may introduce redundant patterns and additional computational costs. Methods: This study proposes a hybrid ViT-ECA-CF framework with adaptive loss reweighting (ALR) for minority-class WBC classification without data augmentation. The framework integrates efficient channel attention (ECA) to enhance discriminative feature representations and color-feature fusion (CF) to jointly capture morphological and staining-related color information from WBC components, including the nucleus, cytoplasm, and granules. An adaptive loss-reweighting strategy dynamically adjusts class contributions according to class distribution, prediction loss, and prediction confidence, thereby directing model optimization toward challenging minority WBC classes. Results: On the evaluated WBC classification dataset, the proposed framework achieved a 90.00% accuracy, 94.49% macro-F1, and 93.98% balanced accuracy, with an F1-score of 96.43% for minority classes. The framework substantially reduced the majority-to-minority performance gap, achieving a minority-to-majority F1 balance ratio of 1.053, indicating a comparable recognition performance across classes despite the severe imbalance. The proposed approach was also evaluated on independent datasets and compared with a CNN baseline in terms of training time. Conclusions: The findings provide an effective strategy by combining transformer-based feature representation, ECA, color-feature fusion, and adaptive loss reweighting to improve minority-class WBC recognition without relying on synthetic data augmentation. The proposed approach offers a potential strategy for handling a severe class imbalance. Further validation on larger and multicenter datasets is required to establish the robustness and clinical applicability of the proposed framework.