Conference proceeding
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1034-1037
13 Jan 2021
Abstract
In this paper1, 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.1Read longer version of this paper: https://arxiv.org/pdf/2004.04582.pdf
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Details
- Title
- DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images
- Creators
- Md Rezaul KarimTill DohmenMichael CochezOya BeyanDietrich Rebholz-SchuhmannStefan DeckerTaesung ParkYoung-Rae ChoXiaohua Tony HuIllhoi YooHyun Goo WooJianxin WangJulio FacelliSeungyoon NamMingon Kang
- Publication Details
- 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1034-1037
- Conference
- 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- Institute of Electrical and Electronics Engineers Inc
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019189051004721