Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19.

Journal: Journal of medical systems
Published Date:

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.

Authors

  • Karim Hammoudi
    Department of Computer Science, IRIMAS, Université de Haute-Alsace, 68100, Mulhouse, France. karim.hammoudi@uha.fr.
  • Halim Benhabiles
    UMR 8520 - IEMN - Institut d'Electronique de Microélectronique et de Nanotechnologie, Université Lille, CNRS, Centrale Lille, Université Polytechnique Hauts-de-France, Junia, F-59000, Lille, France.
  • Mahmoud Melkemi
    Department of Computer Science, IRIMAS, Université de Haute-Alsace, 68100, Mulhouse, France.
  • Fadi Dornaika
    University of the Basque Country, UPV/EHU, Manuel Lardizabal 1, 20018 San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Maria Diza de Haro, 3, 48013 Bilbao, Spain. Electronic address: fadi.dornaika@ehu.es.
  • Ignacio Arganda-Carreras
    Ikerbasque, Basque Foundation for Science, Bilbao 48013, Spain.
  • Dominique Collard
    LIMMS/CNRS-IIS UMI 2820, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba Meguro Ku, Tokyo, 153-8505, Japan.
  • Arnaud Scherpereel
    Lille University Hospital (CHU Lille), French National Institute of Health and Medical Research (Inserm), University of Lille, U1189 - ONCO-THAI, 59000, Lille, France.