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.

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.