Trans-YOLOv5:a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images
10.12122/j.issn.1673-4254.2024.07.01
- VernacularTitle:基于全局-局部注意力机制和YOLOv5的宫颈细胞图像异常检测模型
- Author:
Wenran HU
1
;
Rong FU
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Keywords:
cervical cancer screening;
YOLOv5;
image processing;
Transformer
- From:
Journal of Southern Medical University
2024;44(7):1217-1226
- CountryChina
- Language:Chinese
-
Abstract:
The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis.Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology,but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself,but also involves the comparison with the surrounding cells.Herein we present the Trans-YOLOv5 model,an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images.The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods,with a mAP reaching 65.9%and an AR reaching 53.3%,showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.
