Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images

被引：3

作者：

Wang Z. ^{[1
]}

Dong J. ^{[2
,3
]}

Zhang J. ^{[1
]}

机构：

[1] College of Electrical Engineering, Sichuan University, Chengdu

[2] Key Laboratory of Aerospace Medicine of Ministry of Education, Air Force Medical University, Xi’an

[3] Lintong Rehabilitation and Recuperation Center, PLA Joint Logistic Support Force, Xi’an

来源：

Journal of Shanghai Jiaotong University (Science) | 2022年 / 27卷 / 01期

基金：

中国博士后科学基金;

关键词：

A; computed tomography (CT) images; convolutional neural network; COVID-19; deep learning; ensemble model; R; 445; TP; 183;

D O I：

10.1007/s12204-021-2392-3

中图分类号：

学科分类号：

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 8 347 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: 0.989 3), with an increase of about 7% in comparison to single-component models. And the average test accuracy is 95.62% (recall: 0.958 7, precision: 0.955 9), 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. © 2021, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：70 / 80

页数：10

共 23 条

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