An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

Abstract

COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning-based approaches are being widely used to diagnose the COVID-19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over-fitting. Modern artificial intelligence-based techniques can be used to increase the dataset to avoid from the over-fitting problem. This research work presents the use of various deep learning models along with the state-of-the-art augmentation methods, namely, classical and generative adversarial network- (GAN-) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet-121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X-ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID-19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN-based augmented dataset.