High-through cell micronucleus image detection method combining multi-attention mechanism and YOLOv5

Cell micronucleus detection is an significant method used to evaluate damage to human cells by drugs, radiation, toxic substances, etc. Aiming at the problems of small target size, high similarity between classes and small sample data in cell micronucleus image recognition, this paper proposes an improved YOLOv5 cell micronucleus high-through detection algorithm named YOLOv5-CEB. Firstly, the basic network is optimized by replacing loss function, resetting anchor box and performing exploratory experiments on network depth. Then, in order to enhance the feature extraction ability of the network, the Bifpn structure is introduced. After that, the semantic connection of context is enhanced by the mechanism of fusing global and local attention, which makes the network pays more attention to ROI. Finally,a shallow detection layer is added, which improve the accuracy of cell micronucleus recognition by multi-scale detection. Compared with the initial YOLOv5 algorithm, the experimental results show that the average precision, accuracy and recall of the YOLOv5-CEB algorithm are improved by 2%, 2% and 1.3%, respectively.