CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network.

Journal: Computational and mathematical methods in medicine
Published Date:

Abstract

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.

Authors

  • Wu Deng
    Information Center, West China Hospital of Sichuan university, Chengdu, 610000, Sichuan, China.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Weiwei Song
    Department of Radiology, West China Hospital of Sichuan University, Chengdu Sichuan 610000, China.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Jia Xu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P.R. China.