Deep overparameterized blood cell detection algorithm utilizing hybrid attention mechanisms.
10.7507/1001-5515.202412057
- Author:
Shuo ZHU
1
;
Xukang ZHANG
2
;
Zongyang WANG
3
;
Rui JIANG
1
;
Zhengda LIU
1
Author Information
1. Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence, Wuxi University, Wuxi, Jiangsu 214105, P. R. China.
2. College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.
3. Wuxi Xi Yuan Technology by the Company, Wuxi, Jiangsu 214000, P. R. China.
- Publication Type:Journal Article
- Keywords:
Attention mechanism;
Blood cell detection;
Deep learning;
Object detection;
Overparameterization
- MeSH:
Algorithms;
Humans;
Blood Cells/cytology*;
Blood Cell Count/methods*;
Neural Networks, Computer;
Deep Learning;
Detection Algorithms
- From:
Journal of Biomedical Engineering
2025;42(5):936-944
- CountryChina
- Language:Chinese
-
Abstract:
To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.
