Automatic Detection of COVID-19 Infection Using Chest X-Ray Images Through Transfer Learning  

作　　者：Elene Firmeza Ohata Gabriel Maia Bezerra João Victor Souza das Chagas Aloísio Vieira Lira Neto Adriano Bessa Albuquerque Victor Hugo Cde Albuquerque Pedro Pedrosa Rebouças Filho 

机构地区：[1]the Universidade Federal do Ceará,Fortaleza,CE 60455-970,and also with the Instituto Federal do Ceará,Fortaleza,CE 60040-215,Brazil [2]the Instituto Federal do Ceará,Fortaleza,CE 60040-215,Brazil [3]the Universidade de Fortaleza,Fortaleza,CE 60811-905,Brazil

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第1期239-248,共10页自动化学报（英文版）

基　　金：supported in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior-Brasil(CAPES)(001);the Brazilian National Council for Research and Development(CNPq)(431709/2018-1,311973/2018-3,304315/2017-6,430274/2018-1)。

摘　　要：The new coronavirus(COVID-19),declared by the World Health Organization as a pandemic,has infected more than 1 million people and killed more than 50 thousand.An infection caused by COVID-19 can develop into pneumonia,which can be detected by a chest X-ray exam and should be treated appropriately.In this work,we propose an automatic detection method for COVID-19 infection based on chest X-ray images.The datasets constructed for this study are composed of194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients.Since few images of patients with COVID-19 are publicly available,we apply the concept of transfer learning for this task.We use different architectures of convolutional neural networks(CNNs)trained on Image Net,and adapt them to behave as feature extractors for the X-ray images.Then,the CNNs are combined with consolidated machine learning methods,such as k-Nearest Neighbor,Bayes,Random Forest,multilayer perceptron(MLP),and support vector machine(SVM).The results show that,for one of the datasets,the extractor-classifier pair with the best performance is the Mobile Net architecture with the SVM classifier using a linear kernel,which achieves an accuracy and an F1-score of 98.5%.For the other dataset,the best pair is Dense Net201 with MLP,achieving an accuracy and an F1-score of 95.6%.Thus,the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

关 键 词：Convolutional neural networks(CNNs) COVID-19 transfer learning X-RAY 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] R563.1[医药卫生—呼吸系统] R816.4[医药卫生—内科学]