UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection
Article
Abdar, Moloud, Salari, Soorena, Qahremani, Sina, Lam, Hak-Keung, Karray, Fakhri, Hussain, Sadiq, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir and Nahavandi, Saeid. 2023. "UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection." Information Fusion. 90, pp. 364-381. https://doi.org/10.1016/j.inffus.2022.09.023
| Article Title | UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection |
|---|---|
| ERA Journal ID | 20983 |
| Article Category | Article |
| Authors | Abdar, Moloud, Salari, Soorena, Qahremani, Sina, Lam, Hak-Keung, Karray, Fakhri, Hussain, Sadiq, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir and Nahavandi, Saeid |
| Journal Title | Information Fusion |
| Journal Citation | 90, pp. 364-381 |
| Number of Pages | 364-381 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1566-2535 |
| 1872-6305 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2022.09.023 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253522001609 |
| Abstract | The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification. |
| Keywords | COVID-19; Deep learning; Early fusion; Feature fusion; Uncertainty quantification |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| Concordia University, Canada | |
| Sharif University of Technology, Iran | |
| King’s College London, United Kingdom | |
| University of Waterloo, Canada | |
| Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates | |
| Dibrugarh University, India | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan | |
| University of Quebec, Canada |
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