Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.

Journal: Computers in biology and medicine
Published Date: Apr 1, 2022

Abstract

BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.

Authors

  • Maryam Fallahpoor
    Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
  • Subrata Chakraborty
    Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
  • Mohammad Tavakoli Heshejin
    Department of Information Technology, Telecommunication Company of Iran, Tehran, Iran.
  • Hossein Chegeni
    Fellowship of Interventional Radiology Imaging Center, IranMehr General Hospital, Iran.
  • Michael James Horry
    Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
  • Biswajeet Pradhan
    School of Systems, Management, and Leadership, Faculty of Engineering and IT, University of Technology Sydney, New South Wales, Australia; Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro Gwangjin-gu, 05006 Seoul, South Korea.

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