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 de...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.展开更多
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_17171the Hope Foundation for Cancer Research of UK under Grant No.RM60G0680+5 种基金the British Heart Foundation Accelerator Award of UK under Grant No.A A/18/3/34220Sino-UK Industrial Fund under Grant No.RP202G0289the Global Challenges Research Fund(GCRF)of UK under Grant No.P202PF11the Fundamental Research Funds for the Central Universities of China under Grant No.CDLS-2020-03the 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.182102310629the National Natural Science Foundation of China under Grant Nos.U19B2032 and 61772511.
文摘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.
