  Ultrasound is fast becoming an inevitable diagnostic tool for regular and
continuous monitoring of the lung with the recent outbreak of COVID-19. In this
work, a novel approach is presented to extract acoustic propagation-based
features to automatically highlight the region below pleura, which is an
important landmark in lung ultrasound (LUS). Subsequently, a multichannel input
formed by using the acoustic physics-based feature maps is fused to train a
neural network, referred to as LUSNet, to classify the LUS images into five
classes of varying severity of lung infection to track the progression of
COVID-19. In order to ensure that the proposed approach is agnostic to the type
of acquisition, the LUSNet, which consists of a U-net architecture is trained
in an unsupervised manner with the acoustic feature maps to ensure that the
encoder-decoder architecture is learning features in the pleural region of
interest. A novel combination of the U-net output and the U-net encoder output
is employed for the classification of severity of infection in the lung. A
detailed analysis of the proposed approach on LUS images over the infection to
full recovery period of ten confirmed COVID-19 subjects shows an average
five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 93%,
and 98% respectively over 5000 frames of COVID-19 videos. The analysis also
shows that, when the input dataset is limited and diverse as in the case of
COVID-19 pandemic, an aided effort of combining acoustic propagation-based
features along with the gray scale images, as proposed in this work, improves
the performance of the neural network significantly and also aids the labelling
and triaging process.
