文献类型：会议论文

英文题名：Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration

作者：Wang, Ningwei[1];Liu, Hongzhe[1];Xu, Cheng[1]

第一作者：Wang, Ningwei

通讯作者：Liu, HZ[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：10th IEEE International Conference on Electronics Information and Emergency Communication (IEEE ICEIEC)

会议日期：JUL 17-19, 2020

会议地点：ELECTR NETWORK

语种：英文

外文关键词：deep learning; covid-19 detection; covid-net; transfer learning; model integration

摘要：We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.