南方医科大学学报2024，Vol.44Issue(7)：1217-1226,10.DOI:10.12122/j.issn.1673-4254.2024.07.01
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
Trans-YOLOv5:a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images
摘要
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.关键词
宫颈细胞图像异常检测/YOLOv5/图像处理/全局和局部特征融合Key words
cervical cancer screening/YOLOv5/image processing/Transformer引用本文复制引用
胡雯然,傅蓉..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[J].南方医科大学学报,2024,44(7):1217-1226,10.基金项目
Supported by National Natural Science Foundation of China(82172020). 国家自然科学基金(82172020) （82172020）
