An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral...

Acute Lymphoblastic Leukemia (ALL) is one of the most aggressive hematological malignancies, and its early diagnosis remains challenging due to non-specific clinical symptoms and reliance on invasive procedures such as bone marrow biopsies. To address these limitations, we propose Meta-Conformer-XAI, a novel meta-learned hybrid deep learning framework for non-invasive ALL detection using microscopic peripheral blood smear images. Unlike conventional CNN-Transformer pipelines, our approach integrates three key innovations: (1) a Dual Attention Feature Fusion (DAFF) block that adaptively combines local morphological features extracted by a CNN with global contextual dependencies captured by a Vision Transformer (ViT); (2) a Meta-Learning Path Controller, which dynamically optimizes information flow between convolutional and transformer pathways for improved generalization across heterogeneous datasets; and (3) a Reinforcement Learning-based Confidence Estimator, ensuring robust decision reliability in clinical settings. We validated the framework on two benchmark datasets, the ALL Image Dataset and the C-NMC Leukemia Dataset using both fixed train/validation/test splits and 5-fold cross-validation. To mitigate class imbalance, a class-aware augmentation strategy was employed, significantly improving minority-class recognition. Meta-Conformer-XAI achieved 0.9924 accuracy on the ALL dataset and 0.9636 accuracy on the C-NMC dataset, with AUC-ROC scores exceeding 0.99 across both, outperforming baseline CNNs, ViTs, and existing hybrid architectures. Furthermore, the framework incorporates a comprehensive explainability module combining Grad-CAM, SHAP, LIME, and Integrated Gradients, providing transparent insights into feature attribution and clinical relevance. Overall, Meta-Conformer-XAI advances the state of the art in automated leukemia diagnosis by offering a precise, interpretable, and scalable tool that addresses current limitations of diagnostic invasiveness, model generalization, and clinical trustworthiness.