Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method to generate synthetic COVID-19 chest CT images to augment training data, aiming to improve deep learning models' accuracy in diagnosing COVID-19.

Contribution

It presents a novel approach using GANs to create synthetic chest CT images for COVID-19, addressing data scarcity issues in training deep learning classifiers.

Findings

Approximately 40% of generated images are correctly predicted as COVID-19 positive.

Synthetic images can enhance training datasets for better diagnostic accuracy.

The method helps mitigate data limitations in COVID-19 imaging datasets.

Abstract

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.