  The global pandemic of COVID-19 is continuing to have a significant effect on
the well-being of global population, increasing the demand for rapid testing,
diagnosis, and treatment. Along with COVID-19, other etiologies of pneumonia
and tuberculosis constitute additional challenges to the medical system. In
this regard, the objective of this work is to develop a new deep transfer
learning pipeline to diagnose patients with COVID-19, pneumonia, and
tuberculosis, based on chest x-ray images. We observed in some instances
DenseNet and Resnet have orthogonal performances. In our proposed model, we
have created an extra layer with convolutional neural network blocks to combine
these two models to establish superior performance over either model. The same
strategy can be useful in other applications where two competing networks with
complementary performance are observed. We have tested the performance of our
proposed network on two-class (pneumonia vs healthy), three-class (including
COVID-19), and four-class (including tuberculosis) classification problems. The
proposed network has been able to successfully classify these lung diseases in
all four datasets and has provided significant improvement over the benchmark
networks of DenseNet, ResNet, and Inception-V3. These novel findings can
deliver a state-of-the-art pre-screening fast-track decision network to detect
COVID-19 and other lung pathologies.
