  COVID-19 is a highly contagious respiratory infection that has affected a
large population across the world and continues with its devastating
consequences. It is imperative to detect COVID-19 at the earliest to limit the
span of infection. In this work, a new classification technique CB-STM-RENet
based on deep Convolutional Neural Network (CNN) and Channel Boosting is
proposed for the screening of COVID-19 in chest X-Rays. In this connection, to
learn the COVID-19 specific radiographic patterns, a new convolution block
based on split-transform-merge (STM) is developed. This new block
systematically incorporates region and edge-based operations at each branch to
capture the diverse set of features at various levels, especially those related
to region homogeneity, textural variations, and boundaries of the infected
region. The learning and discrimination capability of the proposed CNN
architecture is enhanced by exploiting the Channel Boosting idea that
concatenates the auxiliary channels along with the original channels. The
auxiliary channels are generated from the pre-trained CNNs using Transfer
Learning. The effectiveness of the proposed technique CB-STM-RENet is evaluated
on three different datasets of chest X-Rays namely CoV-Healthy-6k,
CoV-NonCoV-10k, and CoV-NonCoV-15k. The performance comparison of the proposed
CB-STM-RENet with the existing techniques exhibits high performance both in
discriminating COVID-19 chest infections from Healthy, as well as, other types
of chest infections. CB-STM-RENet provides the highest performance on all these
three datasets; especially on the stringent CoV-NonCoV-15k dataset. The good
detection rate (97%), and high precision (93%) of the proposed technique
suggest that it can be adapted for the diagnosis of COVID-19 infected patients.
The test code is available at
https://github.com/PRLAB21/COVID-19-Detection-System-using-Chest-X-Ray-Images.
