Peripheral Blood Cells and Antibodies Detection Approach Based on Deep Convolutional Neural Network
Objective To solve the problems of time-consuming detection,few cell types and time-consuming manual microscopic examination methods.Methods In order to detect blood cells more quickly,reduce the workload of doctors and provide accurate reports,this study combined the regional convolutional neural network,YOLO,single shot multiBox detector(SSD)and other deep learning methods to detect blood cells.In this experiment,the peripheral blood cell data set was selected,and the SSD model and five network models in the YOLO series,YOLOv5,YOLOX,YOLOv6 and YOLOv7 were used for training.The advantages and disadvantages of the network were discussed by comparing the evaluation indicators.Results This study built a new model with higher accuracy and faster running speed.The accuracy reached 99.3%,the single-image detection time was 10.3 ms,and the memory occupied was only 71.2 MB,surpassing other network models.An ablation experiment was designed to verify the practicality of the newly added fully connected layer module and generalization module.Conclusion This model can detect blood cells excellently and accurately,with fast detection speed and high accuracy.The model is small and easy to use and maintain.
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