An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans
This work addresses COVID-19 severity prediction for medical diagnosis, but it is incremental as it builds on existing ensemble and augmentation techniques.
The paper tackled COVID-19 severity prediction from chest CT scans using an ensemble deep learning model, achieving results comparable to complex methods and securing fourth place in the STOIC2021 challenge.
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.