Medicine and Health Sciences Commons^™

The Effectiveness Of Image Augmentation In Deep Learning Networks For Detecting Covid-19: A Geometric Transformation Perspective, Mohamed Elgendi, Muhammad Umer Nasir, Qunfeng Tang, David Smith, John Paul Grenier, Catherine Batte, Bradley Spieler, William Donald Leslie, Carlo Menon, Richard Ribbon Fletcher, Newton Howard, Rabab Ward, William Parker, Savvas Nicolaou

School of Medicine Faculty Publications

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often …