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Abnormalities in WBCs can be indicative of various conditions, including leukemia. WBCs classification is pivotal for diagnosing hematological disorders. This study advances automated WBCs analysis through an 8-class classification framework encompassing rare but clinically critical subtypes: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (IGs), erythroblasts, and platelets. Leveraging a dataset of 17,092 CellaVision DM96-generated images standardized for clinical relevance, we implement rigorous preprocessing (normalization, resizing) and dynamic augmentation (rotations, flips) to enhance robustness. Six architectures are evaluated: ResNet50, InceptionV3, EfficientNetB3, MobileNetV3, Swin Transformer, and a custom convolutional neural network (CNN). ResNet50 emerged as the top performer 98.83% accuracy, followed by InceptionV3 98.77% and Swin Transformer 98.71%, demonstrating the efficacy of transfer learning and transformer-based attention mechanisms. Class-weighted loss mitigated dataset imbalance, achieving\u2009&gt;\u20090.98 F1-scores for 6\/8 classes. Computational efficiency analysis revealed MobileNetV3 as optimal for deployment (3.43 ms\/inference). The study addresses key challenges\u2014class imbalance, model interpretability via Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations\u2014and validates improved diagnostic precision over prior work. By integrating clinically critical subtypes and state-of-the-art architectures, it provides a robust tool for medical education and practice, enabling early detection of leukemia, sepsis, and myelodysplastic syndromes. This study can enhance the training of medical students and doctors, equipping them with better tools for diagnosis and decision-making. Furthermore, the ability to classify a broader range of WBCs types could lead to more accurate and early diagnoses of diseases, ultimately improving patient care.<\/jats:p>","DOI":"10.1186\/s40537-025-01235-1","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T06:50:50Z","timestamp":1753685450000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating deep learning and transfer learning: optimizing white blood cells classification in medical educational institutions"],"prefix":"10.1186","volume":"12","author":[{"given":"M.","family":"Hussein","sequence":"first","affiliation":[]},{"given":"Faten Abd El-Sattar Zahran","family":"El-Mougi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"1235_CR1","doi-asserted-by":"publisher","unstructured":"Nan Lu HM, Tay, Petchakup C. 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