(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognitio...(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.展开更多
基金This study is partially supported by the Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).We thank Dr.Hemil Patel for his help in English correction.
文摘(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.
