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Related Experiment Video

Updated: Dec 13, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays.

Sivaramakrishnan Rajaraman¹, Jen Siegelman², Philip O Alderson³

¹Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD 20894 USA.

IEEE Access : Practical Innovations, Open Solutions

|August 4, 2020

Summary

This summary is machine-generated.

Deep learning models trained on chest X-rays accurately detect COVID-19 pulmonary manifestations. Iterative pruning and ensemble strategies achieved 99.01% accuracy in identifying viral abnormalities.

Keywords:

COVID-19 Convolutional neural network Deep learning Ensemble Iterative pruning

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Area of Science:

Artificial Intelligence
Medical Imaging
Deep Learning

Background:

COVID-19, caused by SARS-CoV-2, presents pulmonary manifestations detectable via chest X-rays (CXRs).
Accurate and efficient detection of COVID-19 on CXRs is crucial for timely diagnosis and patient management.

Purpose of the Study:

To develop and evaluate an AI model for detecting COVID-19 pulmonary abnormalities using CXRs.
To enhance model performance and efficiency through iterative pruning and ensemble learning.

Main Methods:

Utilized custom and ImageNet-pretrained convolutional neural networks (CNNs) trained on CXR datasets.
Employed knowledge transfer and fine-tuning for classifying normal, bacterial pneumonia, and COVID-19 CXRs.
Applied iterative pruning to reduce model complexity and ensemble strategies to combine predictions.

Main Results:

The best-performing pruned models, combined via weighted averaging, achieved 99.01% accuracy.
An area under the curve (AUC) of 0.9972 was obtained in detecting COVID-19 findings.
The integrated approach of knowledge transfer, pruning, and ensembling improved prediction accuracy.

Conclusions:

Iteratively pruned deep learning model ensembles demonstrate high efficacy in detecting COVID-19 on CXRs.
This AI-driven approach offers a promising tool for rapid COVID-19 screening using chest radiographs.
The model's efficiency and accuracy suggest potential for clinical adoption in disease detection.