BCG-YOLO: an efficient YOLO-based framework for multi-class blood cell detection in microscopic images.
4 affiliations
- 1Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, College of Pharmacy, Changsha Medical University, Changsha, Hunan, China.
- 2Hunan Key Laboratory of Biomedical, Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China.
- 3Institute for Future Sciences, University of South China, Changsha, Hunan, China.
- 4Department of Transfusion Medicine, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, Hunan, China.
Abstract
Objectives
Automated blood cell detection from microscopic images is essential for routine hematological examination and computer-assisted laboratory diagnosis. However, accurate detection remains challenging because of the small size, dense distribution, overlap, and indistinct boundaries of blood cells. This study aimed to develop an efficient deep learning framework for accurate multi-class detection of blood cells in microscopic images.Methods
We developed BCG-YOLO, an enhanced detector based on YOLO11n that integrates a lightweight backbone network, an improved multi-scale feature fusion module, and a dynamic detection head to improve feature representation and adaptive detection of small and densely distributed blood cells. The proposed model was evaluated on the public BCCD dataset and compared with representative one-stage detectors, transformer-based methods, and real-time object detection models. Ablation studies were conducted to assess the effectiveness of each proposed component. Additional five-fold cross-validation and paired image-level bootstrap analyses were performed for YOLO11n and BCG-YOLO.Results
On the original fixed test split, BCG-YOLO achieved an [email protected] of 95.3%, an [email protected]:0.95 of 67.6%, and a recall of 89.8% while requiring only 3.80 GFLOPs. Under the common training and evaluation protocol used in this study, BCG-YOLO achieved the best overall detection performance among all evaluated models. Ablation experiments further demonstrated the effectiveness of the proposed modules, confirming consistent improvements in overall detection performance and computational efficiency.Conclusions
BCG-YOLO provided a favorable balance between detection accuracy and computational cost in the internal evaluation on BCCD. These findings support its feasibility for microscopic blood-cell detection, while independent external validation is required to establish generalizability across laboratories, staining protocols, and imaging systems.
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BibTeX
@article{chen2026bcgyolo,
title = {{BCG-YOLO: an efficient YOLO-based framework for multi-class blood cell detection in microscopic images.}},
author = {Zhu Chen and Yiliang Bao and Yi Liu and Zhongyu Li and Dini Zhang and Meijing Xiao and Yuan Guo and Jinque Luo},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1934762},
url = {https://europepmc.org/article/MED/42898361},
note = {CC BY},
}
RIS
TY - JOUR
TI - BCG-YOLO: an efficient YOLO-based framework for multi-class blood cell detection in microscopic images.
AU - Zhu Chen
AU - Yiliang Bao
AU - Yi Liu
AU - Zhongyu Li
AU - Dini Zhang
AU - Meijing Xiao
AU - Yuan Guo
AU - Jinque Luo
PY - 2026
DA - 2026/10/05
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1934762
UR - https://europepmc.org/article/MED/42898361
AB - Objectives Automated blood cell detection from microscopic images is essential for routine hematological examination and computer-assisted laboratory diagnosis. However, accurate detection remains challenging because of the small size, dense distribution, overlap, and indistinct boundaries of blood cells. This study aimed to develop an efficient deep learning framework for accurate multi-class detection of blood cells in microscopic images.Methods We developed BCG-YOLO, an enhanced detector based on YOLO11n that integrates a lightweight backbone network, an improved multi-scale feature fusion module, and a dynamic detection head to improve feature representation and adaptive detection of small and densely distributed blood cells. The proposed model was evaluated on the public BCCD dataset and compared with representative one-stage detectors, transformer-based methods, and real-time object detection models. Ablation studies were conducted to assess the effectiveness of each proposed component. Additional five-fold cross-validation and paired image-level bootstrap analyses were performed for YOLO11n and BCG-YOLO.Results On the original fixed test split, BCG-YOLO achieved an [email protected] of 95.3%, an [email protected]:0.95 of 67.6%, and a recall of 89.8% while requiring only 3.80 GFLOPs. Under the common training and evaluation protocol used in this study, BCG-YOLO achieved the best overall detection performance among all evaluated models. Ablation experiments further demonstrated the effectiveness of the proposed modules, confirming consistent improvements in overall detection performance and computational efficiency.Conclusions BCG-YOLO provided a favorable balance between detection accuracy and computational cost in the internal evaluation on BCCD. These findings support its feasibility for microscopic blood-cell detection, while independent external validation is required to establish generalizability across laboratories, staining protocols, and imaging systems.
SN - 2296-858X
ER -