  The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put
tremendous pressure on the medical system. Imaging plays a complementary role
in the management of patients with COVID-19. Computed tomography (CT) and chest
X-ray (CXR) are the two dominant screening tools. However, difficulty in
eliminating the risk of disease transmission, radiation exposure and not being
costeffective are some of the challenges for CT and CXR imaging. This fact
induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due
to its practical advantages of noninvasiveness, repeatability, and sensitive
bedside property. In this paper, we utilize a deep learning model to perform
the classification of COVID-19 from LUS data, which could produce objective
diagnostic information for clinicians. Specifically, all LUS images are
processed to obtain their corresponding local phase filtered images and radial
symmetry transformed images before fed into the multi-scale residual
convolutional neural network (CNN). Secondly, image combination as the input of
the network is used to explore rich and reliable features. Feature fusion
strategy at different levels is adopted to investigate the relationship between
the depth of feature aggregation and the classification accuracy. Our proposed
method is evaluated on the point-of-care US (POCUS) dataset together with the
Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance
for COVID-19 prediction.
