  In the current COVID-19 pandemic situation, there is an urgent need to screen
infected patients quickly and accurately. Using deep learning models trained on
chest X-ray images can become an efficient method for screening COVID-19
patients in these situations. Deep learning approaches are already widely used
in the medical community. However, they require a large amount of data to be
accurate. The open-source community collectively has made efforts to collect
and annotate the data, but it is not enough to train an accurate deep learning
model. Few-shot learning is a sub-field of machine learning that aims to learn
the objective with less amount of data. In this work, we have experimented with
well-known solutions for data scarcity in deep learning to detect COVID-19.
These include data augmentation, transfer learning, and few-shot learning, and
unsupervised learning. We have also proposed a custom few-shot learning
approach to detect COVID-19 using siamese networks. Our experimental results
showcased that we can implement an efficient and accurate deep learning model
for COVID-19 detection by adopting the few-shot learning approaches even with
less amount of data. Using our proposed approach we were able to achieve 96.4%
accuracy an improvement from 83% using baseline models.
