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

Year 2020
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Amid the coronavirus disease (COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. The challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography (CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural network (DNN)-based method for automatic detection of COVID-19 symptoms from 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, before being augmented and classified 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 of the predictions. Evaluation results based on hold-out data show that our approach can identify COVID-19 confidently with a positive predictive value (PPV) of 91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and 94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID-19 and, in more general, towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice.

Details

IEEE International Conference on Bioinformatics and Biomedicine (BIBM'2020)

Presented at

IEEE International Conference on Bioinformatics and Biomedicine (BIBM'2020), 2020 , Seoul , KR.

Published in

IEEE International Conference on Bioinformatics and Biomedicine (BIBM'2020) ; IEEE .

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