典型文献
Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images
文献摘要：
Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37％ (recall:0.9981;precision:0.9893),with an increase of about 7％in comparison to single-component models.And the average test accuracy is 95.62％ (recall:0.9587;precision:0.9559),with a corresponding increase of 5.2％.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88％.The proposed method may be a promising solution for the diagnosis of COVID-19.
文献关键词：
中图分类号：
作者姓名：
WANG Zhiming;DONG Jingjing;ZHANG Junpeng
作者机构：
College of Electrical Engineering,Sichuan University,Chengdu 610056,China;Key Laboratory of Aerospace Medicine of Ministry of Education,Air Force Medical University,Xi'an 710032,China;Lintong Rehabilitation and Recuperation Center,PLA Joint Logistic Support Force,Xi'an 710600,China
文献出处：
引用格式：
[1]WANG Zhiming;DONG Jingjing;ZHANG Junpeng-.Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images)[J].上海交通大学学报（英文版）,2022(01):70-80
A类：
Diagnose
B类：
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AB值：
0.620554
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