• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images
 
  • Details
  • Full
Options
2020
Conference Paper
Title

DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images

Abstract
In this paper, we proposed an explainable deep neural networks (DNN)-based method for automatic detection of COVID-19 symptoms from chest radiography (CXR) images, which we call 'DeepCOVIDExplainer'. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed and augmented before classifying with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps (Grad-CAM ++) and layer-wise relevance propagation (LRP). Further, we provide human-interpretable explanations for the diagnosis. Evaluation results show that our approach can identify COVID-19 cases with a positive predictive value (PPV) of 91.6%, 92.45%, and 96.12%, respectively for normal, pneumonia, and COVID-19 cases, respectively, outperforming recent approaches.
Author(s)
Karim, M.R.
Döhmen, T.
Cochez, M.
Beyan, O.
Rebholz-Schuhmann, D.
Decker, S.
Mainwork
IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Proceedings  
Conference
International Conference on Bioinformatics and Biomedicine (BIBM) 2020  
Open Access
DOI
10.1109/BIBM49941.2020.9313304
Language
English
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024