  In this paper, deep-learning-based approaches namely fine-tuning of
pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end
training of a developed CNN model, have been used in order to classify X-Ray
images into four different classes that include COVID-19, normal, opacity and
pneumonia cases. A dataset containing more than 20,000 X-ray scans was
retrieved from Kaggle and used in this experiment. A two-stage classification
approach was implemented to be compared to the one-shot classification
approach. Our hypothesis was that a two-stage model will be able to achieve
better performance than a one-shot model. Our results show otherwise as VGG16
achieved 95% accuracy using one-shot approach over 5-fold of training. Future
work will focus on a more robust implementation of the two-stage classification
model Covid-TSC. The main improvement will be allowing data to flow from the
output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are
VGG16 models fine-tuned on the Covid-19 dataset.
