BC-YOLO: MBConv-ECA based YOLO framework for blood cell detection
Yazarlar (2)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Signal Image and Video Processing (Q3) | ||
| Dergi ISSN | 1863-1703 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2025 |
| Kabul Tarihi | 31-05-2025 | Yayınlanma Tarihi | 09-06-2025 |
| Cilt / Sayı / Sayfa | 19 / 9 / 712–0 | DOI | 10.1007/s11760-025-04321-2 |
| Makale Linki | https://doi.org/10.1007/s11760-025-04321-2 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| The detection and classification of blood cells from microscopic images plays a vital role in medical diagnosis. However, it is challenging because of the small size, varying shape and dense clustering of cells. Conventional methods rely on manual inspection, which is labor-intensive and depends on expert knowledge. In contrast, Deep Learning (DL) based approaches significantly speed up the process and provide more reliable and consistent results. However, there should be a trade-off between computational costs, model size and accuracy. In this work, we propose the Blood Cell You Only Look Once (BC-YOLO) model built on the YOLOv11 architecture. The backbone of the model is enhanced by replacing traditional convolution structures with MBConv-ECA blocks, resulting in lighter and more efficient feature extraction. This modification reduces computational complexity while increasing accuracy, resulting in … |
| Anahtar Kelimeler |
| Blood cell | ECA | MBConv | Object detection | YOLO |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 20 |
| Scopus | 22 |
| Google Scholar | 28 |