Rule-based BloodCell interpretable and actionable hematological classification using ConvNeXt-Tiny

Muhammad Adnan Tariq

doi:10.1016/j.eswa.2026.134553

Rule-based BloodCell interpretable and actionable hematological classification using ConvNeXt-Tiny

2026-10-01

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AI

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Muhammad Adnan Tariq

S

Sheikh Muhammad Saqib

DOI：10.1016/j.eswa.2026.134553

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The automated identification and classification of peripheral blood cells is critical for diagnosing a range of hematological disorders, from systemic infections to leukemia. Such classification supports diagnosis by providing differential cell counts and by detecting abnormal cell morphology, rather than diagnosing a disease directly. Traditional manual microscopy is labor-intensive and prone to subjective error, while existing deep learning “black-box” models often lack the transparency required for clinical adoption. This study proposes an interpretable, research-oriented decision-support framework leveraging the ConvNeXt-Tiny architecture, optimized with tuned hyperparameters and an input resolution of 64 × 64 pixels to enable rapid, lightweight training. The model was trained on a dataset of 17,107 images, using 13,685 for training and 3,422 for testing across eight classes: Basophils, Eosinophils, Erythroblasts, Immature Granulocytes (IG), Lymphocytes, Monocytes, Neutrophils, and Platelets. On the held-out test set, the model achieved an overall accuracy of 0.98 and a weighted F1-score of 0.98. Reliability was assessed with a Matthews Correlation Coefficient (MCC) of 0.974 and a Cohen’s Kappa of 0.974, indicating strong agreement with the expert-labeled ground truth. A key contribution is the integration of Explainable AI (XAI) through LIME (Local Interpretable Model-Agnostic Explanations), which highlights influential superpixels to spatially validate cellular morphology. These visual explanations are complemented by a rule-based clinical decision-support (expert-system) module that maps the predicted cell class to predefined, non-diagnostic clinical guidance. By combining deep-learning feature extraction with rule-based recommendations drawn from standard hematology references, the framework provides a transparent and lightweight research prototype intended to assist, rather than replace, expert hematological assessment. Compared with existing approaches (Table 6), it addresses all eight clinically relevant classes with competitive accuracy and reliability while adding region-level LIME interpretability and a rule-based decision-support layer that most prior systems lack.

Keywords:

ConvNeXt-Tiny

Blood cell classification

Explainable AI (XAI)

LIME

Rule-based decision support

Hematology

Deep learning

Rule-based BloodCell interpretable and actionable hematological classification using ConvNeXt-Tiny

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