  Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT)
images has garnered a lot of attention in recent times due to the COVID-19
pandemic. Convolutional Neural Networks (CNNs) are well suited for the image
analysis tasks when trained on humongous amounts of data. Applications
developed for medical image analysis require high sensitivity and precision
compared to any other fields. Most of the tools proposed for detection of
COVID-19 claims to have high sensitivity and recalls but have failed to
generalize and perform when tested on unseen datasets. This encouraged us to
develop a CNN model, analyze and understand the performance of it by
visualizing the predictions of the model using class activation maps generated
using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This
study provides a detailed discussion of the success and failure of the proposed
model at an image level. Performance of the model is compared with
state-of-the-art DL models and shown to be comparable. The data and code used
are available at https://github.com/aleesuss/c19.
