An Ensemble Deep Learning Approach for COVID-19 Severity Prediction
  Using Chest CT Scans
Sidra Aleem Mayug Maniparambil Suzanne Little Noel O'Connor Kevin McGuinness
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 predictions. 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. Our code is available on
online: at: https://github.com/aleemsidra/stoic2021- baseline-finalphase-main.