G-DCNN: GAN based Deep 2D-CNN for COVID-19 Classification

Abstract

Recent progress in COVID-19 detection techniques involve deep learning models. The patient’s image data like Chest X-Ray Images, CT-scan data help the physician for analyzing whether the patient is COVID-19 positive or negative. However, huge data size is essential for improving the classification accuracy of deep learning model. Data Augmentation (DA) is a promising solution to generate synthetic samples of data. Sampling is a traditional data augmentation technique to generate synthetic samples. Recently, Generative Adversarial Networks (GAN) have declared in generating high quality synthetic data from acutal small data to treat imbalance issue. This work proposed a method called GAN based Deep 2D-CNN (G-DCNN) for COVID-19 recognition. In this study, GAN has been used for synthesizing Chest X-Ray and CT-scan images followed by Deep 2D-CNN with the goal of detecting COVID-19.