  The early and reliable detection of COVID-19 infected patients is essential
to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not
available in many countries and also there are genuine concerns about their
reliability and performance. Motivated by these shortcomings, this paper
proposes a deep uncertainty-aware transfer learning framework for COVID-19
detection using medical images. Four popular convolutional neural networks
(CNNs) including VGG16, ResNet50, DenseNet121, and InceptionResNetV2 are first
applied to extract deep features from chest X-ray and computed tomography (CT)
images. Extracted features are then processed by different machine learning and
statistical modelling techniques to identify COVID-19 cases. We also calculate
and report the epistemic uncertainty of classification results to identify
regions where the trained models are not confident about their decisions (out
of distribution problem). Comprehensive simulation results for X-ray and CT
image datasets indicate that linear support vector machine and neural network
models achieve the best results as measured by accuracy, sensitivity,
specificity, and AUC. Also it is found that predictive uncertainty estimates
are much higher for CT images compared to X-ray images.
