An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans

Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity prediction from chest CT scans using the STOIC dataset. We developed an ensemble deep learning based model that incorporates multiple neural networks to improve COVID-19 severity prediction. To address data imbalance, we used slicing functions and data augmentation. We further improved performance using test time data augmentation. Our approach, which employs a simple yet effective ensemble of deep learning-based models with strong test time augmentations, achieved results comparable to more complex methods and secured the fourth position in the STOIC2021 COVID-19 AI Challenge. Code is available online at: STOIC AI Challenge