  The recent outbreak of COVID-19 has halted the whole world, bringing a
devastating effect on public health, global economy, and educational systems.
As the vaccine of the virus is still not available, the most effective way to
combat the virus is testing and social distancing. Among all other detection
techniques, the Chest X-ray (CXR) based method can be a good solution for its
simplicity, rapidity, cost, efficiency, and accessibility. In this paper, we
propose CovMUNET, which is a multiple loss deep neural network approach to
detect COVID-19 cases from CXR images. Extensive experiments are performed to
ensure the robustness of the proposed algorithm and the performance is
evaluated in terms of precision, recall, accuracy, and F1-score. The proposed
method outperforms the state-of-the-art approaches with an accuracy of 96.97%
for 3-class classification (COVID-19 vs normal vs pneumonia) and 99.41% for
2-class classification (COVID vs non-COVID). The proposed neural architecture
also successfully detects the abnormality in CXR images.
