摘要
COVID-19 is a contagious infection that has severe effects on the global economy and our daily life.Accurate diagnosis of COVID-19 is of importance for consultants,patients,and radiologists.In this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and RVFL.Therefore,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,respectively.After comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed classifiers.In addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.
作者
Xin Zhang
Siyuan Lu
Shui-Hua Wang
Xiang Yu
Su-Jing Wang
Lun Yao
Yi Pan
Yu-Dong Zhang
张鑫;陆思源;王水花;余翔;王胜菁;姚仑;潘毅;张煜东(Department of Medical Imaging,The Fourth People's Hospital of Huai'an,Huai'an 223002,China;School of Informatics,University of Leicester,Leicester,LE17RH,U.K.;School of Architecture Building and Civil Engineering,Loughborough University,Loughborough,LE113TU,U.K.;School of Mathematics and Actuarial Science,University of Leicester,Leicester,LE17RH,U.K.;Key Laboratory of Behavior Sciences,Institute of Psychology,Chinese Academy of Sciences,Beijing 100101,China;Department of Psychology,University of the Chinese Academy of Sciences,Beijing 100101,China;Department of Infection Diseases,The Fourth People's Hospital of Huai'an,Huaian 223002,China;Department of Computer Science,Georgia State University,Atlanta 30302-5060,U.S.A.;Department of Information Systems,Faculty of Computing and Information Technology,King Abdulaziz University Jeddah 21589,Saudi Arabia)
基金
supported by the Royal Society International Exchanges Cost Share Award of UK under Grant No.RP202G0230,the Medical Research Council Confidence in Concept Award of UK under Grant No.MC_PC_17171
the Hope Foundation for Cancer Research of UK under Grant No.RM60G0680
the British Heart Foundation Accelerator Award of UK under Grant No.A A/18/3/34220
Sino-UK Industrial Fund under Grant No.RP202G0289
the Global Challenges Research Fund(GCRF)of UK under Grant No.P202PF11
the Fundamental Research Funds for the Central Universities of China under Grant No.CDLS-2020-03
the Key Laboratory of Child Development and Learning Science(Southeast University),Ministry of Education of China,Henan Key Research and Development Project of China,under Grant No.182102310629
the National Natural Science Foundation of China under Grant Nos.U19B2032 and 61772511.
