{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:33:59Z","timestamp":1793504039172,"version":"4.5.0"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Projects Commission of Istanbul Topkapi University","award":["GAP2025-003"],"award-info":[{"award-number":["GAP2025-003"]}]},{"award":["GAP2025-003"],"award-info":[{"award-number":["GAP2025-003"]}],"id":[{"id":"https:\/\/ror.org\/00g241p78","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Accurate peripheral blood cell detection remains difficult in dense smear fields, where touching erythrocytes, small platelets, and staining variability reduce the reliability of box-based object detectors. This study aims to address these limitations by proposing BloodContourNet, a contour-informed and density-aware detection framework for red blood cell, white blood cell, and platelet localization. Method: BloodContourNet is built on YOLOv26-L and integrates three complementary modules. The Hematological Attention Module (HAM) refines high-level features using local convolution and shifted-window attention. The Density-Aware Reweighting Head (DARH) modulates the classification loss for locally sparse or difficult instances without image-level resampling. The B\u00e9zier Contour Refinement Head (BCRH) provides a weakly supervised geometric overlap cue for ambiguous dense-region post-processing. Results: Experiments on the TXL-PBC dataset, containing 1260 peripheral smear images and 18,143 annotated cells, benchmarked 25 YOLO variants and 10 Transformer-based or reference detectors under a unified split and evaluation protocol. Across five random seeds, BloodContourNet achieved 99.1% mean Average Precision (mAP) at 50% Intersection-over-Union (IoU) and 87.8% mAP@50:95, improving strict-IoU performance by 1.4 points over YOLOv26-L and 0.7 points over DINO-DETR. Both margins were statistically significant across the five seeds (paired two-sided t-tests, both p &lt; 0.05), and the five-seed 95% confidence interval of the proposed model was [87.5, 88.0]. Conclusions: BloodContourNet primarily improved the strict-localization setting on TXL-PBC, where dense erythrocyte regions and small platelet instances remain challenging for box-based detectors. External patient- or slide-independent validation remains necessary before broader clinical claims can be made.<\/jats:p>","DOI":"10.3390\/diagnostics16152362","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T16:11:56Z","timestamp":1785168716000},"page":"2362","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BloodContourNet: A YOLO-Based Density-Aware B\u00e9zier Refinement Framework for Peripheral Blood Cell Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0562-4931","authenticated-orcid":false,"given":"Mustafa","family":"Yurdakul","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Natural Sciences, K\u0131r\u0131kkale University, 71000 K\u0131r\u0131kkale, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eda","family":"D\u00f6nmez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, K\u0131r\u0131kkale University, 71000 K\u0131r\u0131kkale, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4364-9144","authenticated-orcid":false,"given":"Merve","family":"Ersoy","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering and Natural Sciences, Istanbul Topkap\u0131 University, 34000 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-2091","authenticated-orcid":false,"given":"Faruk","family":"\u00d6zger","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, I\u011fd\u0131r University, 76000 I\u011fd\u0131r, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6670-2169","authenticated-orcid":false,"given":"Ishak","family":"Pacal","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, I\u011fd\u0131r University, 76000 I\u011fd\u0131r, T\u00fcrkiye"},{"name":"Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Fenerbahce University, 34758 Istanbul, T\u00fcrkiye"},{"name":"Department of Electronics and Information Technologies, Faculty of Architecture and Engineering, Nakhchivan State University, Nakhchivan 7012 AZ, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bain, B.J., and Leach, M. 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