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

: Karim, M.R.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.; Decker, S.


Park, T. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Proceedings : December 16-19, 2020, Virtual Event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6216-4
ISBN: 978-1-7281-6215-7
International Conference on Bioinformatics and Biomedicine (BIBM) <2020, Online>
Conference Paper
Fraunhofer FIT ()

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.