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

Yonglin Wu¹, Dongxu Gao², Yinfeng Fang³

¹School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110158, China.

Biomimetics (Basel, Switzerland)

|September 27, 2023

Summary

This study introduces SDE-YOLO, an enhanced blood cell detection algorithm. It significantly improves accuracy and speed for identifying white blood cells, red blood cells, and platelets.

Keywords:

EIOU PAN Swin Transformer blood cell testing depth-separable convolution

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Area of Science:

Computer Vision and Image Analysis
Biomedical Engineering
Machine Learning for Healthcare

Background:

Existing single-stage and two-stage algorithms exhibit limitations in blood cell detection, including low accuracy, misdetection, and missed detections.
Accurate and efficient blood cell detection is crucial for clinical diagnostics and research.

Purpose of the Study:

To develop an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework.
To enhance the accuracy and real-time performance of blood cell detection, specifically for white blood cells, red blood cells, and platelets.

Main Methods:

Integration of Swin Transformer into the backbone for superior feature extraction.
Modification of the path-aggregation network (PANet) by removing a network layer and employing depth-separable convolution to improve small target detection and reduce parameters.
Replacement of the complete intersection over union (CIoU) loss function with the Euclidean intersection over union (EIoU) loss function to address sample imbalance and accelerate convergence.

Main Results:

SDE-YOLO achieved high mean Average Precision (mAP) scores: 99.5% for white blood cells, 95.3% for red blood cells, and 93.3% for platelets on the BCCD dataset.
Demonstrated superior performance compared to established algorithms like SSD, YOLOv4, and YOLOv5s.
Outperformed YOLOv7 and YOLOv8 in terms of accuracy and real-time detection capabilities for blood cells.

Conclusions:

The SDE-YOLO algorithm effectively addresses the challenges in blood cell detection, offering significant improvements in accuracy and efficiency.
The proposed modifications, including Swin Transformer integration, PANet optimization, and EIoU loss, contribute to enhanced small target recognition and faster convergence.
SDE-YOLO presents a promising solution for real-time, high-accuracy blood cell analysis in clinical and research settings.