Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

Journal: The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
PMID: 29471912

Abstract

OBJECTIVE: To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients.

Authors

  • A S Becker
    Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland.
  • C Blüthgen
    Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland.
  • V D Phi van
    Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland.
  • C Sekaggya-Wiltshire
    Infectious Disease Institute, College of Health Sciences, Makerere University, Kampala, Uganda.
  • B Castelnuovo
    Infectious Disease Institute, College of Health Sciences, Makerere University, Kampala, Uganda.
  • A Kambugu
    Infectious Disease Institute, College of Health Sciences, Makerere University, Kampala, Uganda.
  • J Fehr
    Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • T Frauenfelder
    Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland.

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