Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia

Min Zhou; Yong Chen; Dexiang Wang; Yanping Xu; Weiwu Yao; Jingwen Huang; Xiaoyan Jin; Zilai Pan; Jingwen Tan; Lan Wang; Yihan Xia; Longkuan Zou; Xin Xu; Jingqi Wei; Mingxin Guan; Jianxing Feng; Huan Zhang; Jieming Qu

The copyright holder for this preprint . https://doi.org/10.1101/2020.03. 24.20043117 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Train Test EA S1 S2 S3 S4 S5 R1 R2 R3 R4 R5 Plain Trinary IP Center1 iCT 256 8 3 3 1 1 1 1 0 0 1 1 1 0 1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 SOMATOM Perspective 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 Center 8 uCT 528 0 0 3 1 1 1 1 1 1 1 1 1 1 1 0 uCT 510 0 0 2 0 0 0 0 0 0 0 0 0 0 2 1 Total 35 15 57 14 9 16 17 26 26 24 20 25 27 16 10 Train, Test, and EV individually shows number of NCP or IP cases in the training set, the test set and the external validation set. Number of misclassified patients is presented on the specialist group (S1~S5), the resident group (R1~R5) and both deep learning schemes on the external validation group. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.