UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

Abstract--The COVID-19 (Coronavirus disease 2019) pandemic Index Terms--COVID-19, Deep learning, Early fusion, Feature has become a major global threat to human health and fusion, Uncertainty quantification. Such automatic systems are usually based on traditional machine learning or deep learning methods. We argue that the uncertainty of the model's predictions PCR has a low sensitivity. H.K. Lam is with the Centre for Robotics Research, Department of F. Karray is with the Centre for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of S. Hussain is with the System Administrator, Dibrugarh University, U. R. Acharya is with the Department of Electronics and Computer V. Makarenkov is with the Department of Computer Science, In recent years, deep learning models have had the Its areas of research and application have been growing widespread applicability not only in medical imaging field drastically. These models have allowed the information fusion to change from centralized also been extensively applied for COVID-19 detection. It is single node information fusion to distributed information critical to discriminate COVID-19 from other forms of pneumonia fusion. Farooq et al. [8] introduced an open-access Modern medicine nowadays depends on amalgamation dataset and the open-source code of their implementation of data and information from manifold sources that include using a CNN framework for distinguishing COVID-19 from structured imaging data, laboratory data, unstructured analogous pneumonia cohorts from chest X-ray images. The narrative data, and even observational or audio authors designed their COVIDResNet model by utilizing a data in some cases [22].