  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 to accurately distinguish 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 existing
studies, which used either CT scan or X-ray images in COVID-19-case
classification, we present a 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 existing studies have overlooked it. We quantify the prediction
uncertainty in our feature fusion model using effective Ensemble MC 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.
