中国医疗设备2024，Vol.39Issue(12)：22-27,45,7.DOI:10.3969/j.issn.1674-1633.2024.12.005
基于深度卷积神经网络的外周血液细胞及抗体检测方法
Peripheral Blood Cells and Antibodies Detection Approach Based on Deep Convolutional Neural Network
摘要
Abstract
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.关键词
血细胞检测/目标检测/外周血细胞/深度学习网络模型/消融实验/YOLO/单次多框检测器(SSD)Key words
blood cell detection/object detection/peripheral blood cells/deep learning network model/ablation experiment/YOLO/single shot multibox detector分类
医药卫生引用本文复制引用
李艾彤,李铭诗,李靖鹏,李晨..基于深度卷积神经网络的外周血液细胞及抗体检测方法[J].中国医疗设备,2024,39(12):22-27,45,7.基金项目
国家自然科学基金重点项目(82220108007). （82220108007）
