SDE-YOLO: A Novel Method for Blood Cell Detection.



Wu, Yonglin ORCID: 0009-0000-3556-4912, Gao, Dongxu ORCID: 0000-0001-7008-0737, Fang, Yinfeng ORCID: 0000-0001-5794-8925, Xu, Xue, Gao, Hongwei and Ju, Zhaojie
(2023) SDE-YOLO: A Novel Method for Blood Cell Detection. Biomimetics (Basel, Switzerland), 8 (5). 404-.

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Abstract

This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies.

Item Type: Article
Uncontrolled Keywords: EIOU, PAN, Swin Transformer, blood cell testing, depth-separable convolution
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 09 Apr 2024 09:52
Last Modified: 09 Apr 2024 13:52
DOI: 10.3390/biomimetics8050404
Open Access URL: https://doi.org/10.3390/biomimetics8050404
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3180205