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<p>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.</p>

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

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  • 发表日期：

    2026年6月3日

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摘要

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.