Karim, M.R.M.R.KarimDöhmen, T.T.DöhmenCochez, M.M.CochezBeyan, O.O.BeyanRebholz-Schuhmann, D.D.Rebholz-SchuhmannDecker, S.S.Decker2022-03-152022-03-152020https://publica.fraunhofer.de/handle/publica/41210710.1109/BIBM49941.2020.9313304In 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.en004005006conference paper