Abstract:
COVID-19 had a devastating effect on human lives. Not only it infected millions of
people and took precious lives but it also became quite a task to control its rising
trajectory. Different researchers found that radiography images of infected patients were
quite dissimilar in comparison to normal or viral pneumonia patients. This led scientists
think differently in terms of classifying COVID-19 patients rather than relying on
expensive and time-consuming PCR tests. Deep learning algorithms provides a
completely different dimension to solve critical issues related to human lives. That
dimension is different in terms of smartness and efficiency that drives us to a completely
different solution is in very lesser time. Also, this solution is data-driven so there is a
lesser chance to get the proposed solution wrong. Also, one of the pros of these algorithms
is the classification of multiple groups in data which helps to understand the problem
better. Deep learning solutions have shown excellency in different fields of life and one
of its achievements is in the field of bio-medical image analysis. Using this concept in
mind, we have proposed a series of COVID-19 models to classify positive COVID-19
patients with the help of dataset consisting of radiography images. For this purpose, we
have used 4 different CNN Architectures i.e., AlexNet, ResNet-50, VGG-16, and
Inception V3. We have also used 4 different datasets. So, in total, we have developed 16
models. Another thing that is important to state here is that we have used a new technique
called “Transfer Learning”. Second, third and fourth model were developed with the help
of first model on each of the architectures. The reason here was to make use of the
knowledge learnt with first model. The results received suggested better numbers
compared to predecessor models and proved a point that these models can be relied in
reaching to conclusion.
