CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from Chest X-ray

by   A. Q. M. Sazzad Sayyed, et al.

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 2-class classification (COVID vs non-COVID). The proposed neural architecture also successfully detects the abnormality in CXR images.



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