Selected article for: "CT feature and imaging feature"
Author: Cheng Jin; Weixiang Chen; Yukun Cao; Zhanwei Xu; Xin Zhang; Lei Deng; Chuansheng Zheng; Jie Zhou; Heshui Shi; Jianjiang Feng
Title: Development and Evaluation of an AI System for COVID-19
  • Document date: 2020_3_23
    • ID: k1lg8c7q_32
    Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.20.20039834 doi: medRxiv preprint features and the remaining features have better identification ability and lower correlation ( Figure 9 , Figure 10 ). The selected 15 features were used to explain the imaging characteristics in CT (Feature Analysis in Method). The extracted features show more separable statistical distribution between lesion and normal regions (Figure 6 b).....
    Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.20.20039834 doi: medRxiv preprint features and the remaining features have better identification ability and lower correlation ( Figure 9 , Figure 10 ). The selected 15 features were used to explain the imaging characteristics in CT (Feature Analysis in Method). The extracted features show more separable statistical distribution between lesion and normal regions (Figure 6 b). We extracted three additional features for the attentional regions, distance feature, 2-D margin fractal dimension, and 3-D grayscale mesh fractal dimension (Figure 8 ). According to previous literature [21] on the pathogenesis and morphology of COVID-19, we believe that there may be a statistical rule in the pathogenesis (see Feature Analysis section in Methods).

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