  SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious
disease that is infected by a novel coronavirus, and has been rapidly spreading
across the globe. It is very important to test and isolate people to reduce
spread, and from here comes the need to do this quickly and efficiently.
According to some studies, Chest-CT outperforms RT-PCR lab testing, which is
the current standard, when diagnosing COVID-19 patients. Due to this, computer
vision researchers have developed various deep learning systems that can
predict COVID-19 using a Chest-CT scan correctly to a certain degree. The
accuracy of these systems is limited since deep learning neural networks such
as CNNs (Convolutional Neural Networks) need a significantly large quantity of
data for training in order to produce good quality results. Since the disease
is relatively recent and more focus has been on CXR (Chest XRay) images, the
available chest CT Scan image dataset is much less. We propose a method, by
utilizing GANs, to generate synthetic chest CT images of both positive and
negative COVID-19 patients. Using a pre-built predictive model, we concluded
that around 40% of the generated images are correctly predicted as COVID-19
positive. The dataset thus generated can be used to train a CNN-based
classifier which can help determine COVID-19 in a patient with greater
accuracy.
