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However, fine-grained recognition remains challenging due to strong inter-class similarity, intra-class morphological variability, and confounding cytoplasmic texture patterns that blur decision boundaries among related cell types. We propose SwinDiNO-AFNet for eight-class classification of basophil, eosinophil, erythroblast, immature granulocyte (IG), lymphocyte, monocyte, neutrophil, and platelet. SwinDiNO-AFNet is a dual-path hybrid that fuses hierarchical window-based features from a Swin Transformer with global representations from a pretrained DINOv2 vision transformer used strictly as a feature extractor. The flattened Swin and pooled DINOv2 features are concatenated and refined through a channel attention module to obtain attention-weighted fusion before the final classifier. To ensure rigorous evaluation, the training pipeline enforces leakage control via perceptual-hash based near-duplicate removal before fold creation, with consistent RGB enforcement, 224\u2009\u00d7\u2009224 resizing, ImageNet normalization, and train-only augmentation and robustness noise applied exclusively within training folds. Under leakage-safe stratified fivefold cross-validation, SwinDiNO-AFNet achieves 98.61\u2009\u00b1\u20090.21% validation accuracy with 98.46% macro-F1, and statistical testing (Welch with Holm adjustment) supports improvements over multiple baseline backbones under the same protocol. Precision-recall analysis further indicates near-ceiling separability (micro-AUPRC 0.9979), while confusion matrix and Grad-CAM based error analysis show that remaining failures concentrate in morphologically ambiguous cases, including IG confusions toward granulocytic patterns. Overall, the results show that attention-guided fusion of complementary Swin and DINOv2 representations, combined with leakage-safe cross-validation and XAI-backed diagnostics, provides a strong solution for eight-class peripheral blood cell classification on the studied dataset.<\/jats:p>","DOI":"10.1007\/s44163-026-01211-5","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T21:46:42Z","timestamp":1776808002000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-guided fusion of swin transformer and DINOv2 features for peripheral blood cell classification across eight classes"],"prefix":"10.1007","volume":"6","author":[{"given":"Md Mehedi Hassan","family":"Melon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sazid Rahman","family":"Kazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roise","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossain","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debabrata","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yearanoor","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roba","family":"Keneni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"issue":"1","key":"1211_CR1","volume":"2014","author":"MC Su","year":"2014","unstructured":"Su MC, Cheng CY, Wang PC. 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