Research On Classification And Recognition Algorithm Of Microscopic White Blood Cells Images

Peripheral blood white blood cells play a role that cannot be ignored in the human immune system and are of great value to doctors in diagnosing the disease.At present,hospitals mainly use manual microscopy and cell analyzers to detect white blood cells,but there are problems such as heavy workload,long time,small statistics,and being easily affected by subjective factors.Therefore,there is an urgent practical need to achieve accurate white blood cells classification and identification.In view of the complex appearance and small size of white blood cells,which lead to low recognition accuracy and poor effect,this paper proposes an improved YOLOv5 white blood cells recognition algorithm based on the YOLOv5 network model.First,a coordinate attention mechanism is added to the convolutional layer of the backbone network,which embeds the position information into the channel attention to improve the feature extraction ability of the network.Then,the multi-scale feature detection layers are adopted to make full use of the shallow feature information to reduce the loss of feature information of small target areas during the convolution process,and to improve the identification accuracy of white blood cells with smaller size.In order to solve the problem of class imbalance in the task of white blood cells classification and identification,an unbalanced white blood cells identification algorithm based on YOLOv5-CHCE is proposed on the basis of improving the YOLOv5 white blood cells identification algorithm.The algorithm uses the class-balanced focal loss function as the classification loss function of the network,which rebalances the loss weights of various types of white blood cells to improve the recognition rate of minority classes in the imbalanced white blood cells dataset.At the same time,the effective intersection ratio loss function is used to improve the frame regression loss function of the network to improve the accuracy of the detection frame recognition.This paper uses the public white blood cells dataset and the self-built SWBC(Self-built White Blood Cells)dataset to conduct experimental evaluations on the improved algorithm.The experimental results show that the improved YOLOv5-CHCE model proposed in this paper achieves 98.7%,98.5%and 98.5%of the precision,recall and mean Average Precision(mAP)in the class-imbalanced public white blood cells dataset,respectively.In the self-built SWBC dataset,the precision,recall and mAP reached 93%,94.3%and 97.1%,respectively.The improved network model shows superiority in different datasets and achieves higher-precision classification and identification of multiple types of white blood cells.