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
5/17/2023
eess.IVcs.CVcs.LG

Official code: aleemsidra/STOIC2021-COVID-19-AI-Challenge (1 star, MIT, last commit 2025-01-24).

Abstract

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.

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Code Implementations(1)

aleemsidra/STOIC2021-COVID-19-AI-ChallengeOfficial100%
MIT

This repository contains the pytorch code for our IEEE IMVIP paper "An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans".

10Shell, PythonMay 13, 20221 years agoMIT

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Cite this paper

@article{aleem2023ensemble,
  title  = {An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans},
  author = {Sidra Aleem and Mayug Maniparambil and Suzanne Little and Noel O'Connor and Kevin McGuinness},
  year   = {2023},
  eprint = {2305.10115},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.10115v1}
}

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