基于深度学习的新冠肺炎CT图像识别研究  

Deep Learning Based Novel Coronavirus Pneumonia CT Image Recognition

作　　者：王琪垚 王健庆[1] Wang Qiyao;Wang Jianqing(Zhejiang Chinese Medical University,Hangzhou Zhejiang 310053,China)

机构地区：[1]浙江中医药大学,浙江杭州310053

出　　处：《信息与电脑》2020年第17期62-64,共3页Information & Computer

基　　金：教育部留学回国人员科研启动基金资助项目(2014)。

摘　　要：为辅助医疗人员诊断新冠肺炎患者,本文设计出高效快速的CT图像识别系统。该系统通过对比主流卷积神经网络(CNN)模型,选择GoogleNet和ResNet深度学习模型;通过实验对比算法识别的精确度及相应参数调整,验证模型在识别新冠肺炎CT图像中的有效性。In order to diagnose novel coronavirus pneumonia in the paramedic,this paper designs an efficient and fast CT image recognition system.The deep learning model of novel coronavirus pneumonia(CNN)model is selected by comparing the GoogleNet and ResNet depth learning models.The accuracy of the algorithm and the corresponding parameters are compared with the experimental results,and the validity of the model is demonstrated in the identification of CT images of the new cr own pneumonia.

关 键 词：神经网络 深度学习 新冠肺炎 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]