  Presently, Covid-19 is a serious threat to the world at large. Efforts are
being made to reduce disease screening times and in the development of a
vaccine to resist this disease, even as thousands succumb to it everyday. We
propose a novel method of automated screening of diseases like Covid-19 and
pneumonia from Chest X-Ray images with the help of Computer Vision. Unlike
computer vision classification algorithms which come with heavy computational
costs, we propose a knowledge distillation based approach which allows us to
bring down the model depth, while preserving the accuracy. We make use of an
augmentation of the standard distillation module with an auxiliary intermediate
assistant network that aids in the continuity of the flow of information.
Following this approach, we are able to build an extremely light student
network, consisting of just 3 convolutional blocks without any compromise on
accuracy. We thus propose a method of classification of diseases which can not
only lead to faster screening, but can also operate seamlessly on low-end
devices.
