  Recently, the outbreak of the novel Coronavirus disease 2019 (COVID-19)
pandemic has seriously endangered human health and life. Due to limited
availability of test kits, the need for auxiliary diagnostic approach has
increased. Recent research has shown radiography of COVID-19 patient, such as
CT and X-ray, contains salient information about the COVID-19 virus and could
be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster
imaging time, wide availability, low cost and portability gains much attention
and becomes very promising. Computational methods with high accuracy and
robustness are required for rapid triaging of patients and aiding radiologist
in the interpretation of the collected data. In this study, we design a novel
multi-feature convolutional neural network (CNN) architecture for multi-class
improved classification of COVID-19 from CXR images. CXR images are enhanced
using a local phase-based image enhancement method. The enhanced images,
together with the original CXR data, are used as an input to our proposed CNN
architecture. Using ablation studies, we show the effectiveness of the enhanced
images in improving the diagnostic accuracy. We provide quantitative evaluation
on two datasets and qualitative results for visual inspection. Quantitative
evaluation is performed on data consisting of 8,851 normal (healthy), 6,045
pneumonia, and 3,323 Covid-19 CXR scans. In Dataset-1, our model achieves
95.57\% average accuracy for a three classes classification, 99\% precision,
recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained
94.44\% average accuracy, and 95\% precision, recall, and F1-scores for
detection of COVID-19. Our proposed multi-feature guided CNN achieves improved
results compared to single-feature CNN proving the importance of the local
phase-based CXR image enhancement
(https://github.com/endiqq/Fus-CNNs_COVID-19).
