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Related Experiment Video

Updated: Dec 4, 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

CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia.

Xiang Yu¹, Shui-Hua Wang², Yu-Dong Zhang^1,3

¹School of Informatics, University of Leicester, Leicester, LE1 7RH, UK.

Information Processing & Management

|October 26, 2020

Summary

A new deep learning model, CGNet, accurately detects pneumonia from X-ray images. This AI tool shows high performance in identifying pneumonia, including cases related to COVID-19, aiding timely diagnosis.

Keywords:

COVID-19 Chest X-ray images Feature reconstruction Graph Transfer learning

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Last Updated: Dec 4, 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

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Pneumonia is a significant global health concern, particularly for children.
The COVID-19 pandemic has exacerbated pneumonia-related mortality and morbidity.
Early detection of pneumonia is crucial for effective treatment and disease containment.

Purpose of the Study:

To develop a deep learning framework for automated pneumonia detection using chest X-ray images.
To introduce and evaluate a novel Convolutional Graph Network (CGNet) for binary classification of normal versus pneumonia X-rays.
To assess the model's efficacy in identifying pneumonia, including that caused by COVID-19.

Main Methods:

Utilized transfer learning with state-of-the-art Convolutional Neural Networks (CNNs) for feature extraction.
Implemented a graph-based feature reconstruction approach to combine extracted features.
Employed a Graph Neural Network (GNet) for the final classification of chest X-ray images.

Main Results:

Achieved high accuracy (0.9872), sensitivity (1), and specificity (0.9795) on a public pneumonia dataset (5,856 images).
Demonstrated excellent performance on a COVID-19 CT dataset with accuracy (0.99), specificity (1), and sensitivity (0.98).
The CGNet framework effectively classifies chest X-rays for pneumonia detection.

Conclusions:

The proposed CGNet offers a robust and accurate method for diagnosing pneumonia from chest X-rays.
The model shows promise for rapid and reliable detection of pneumonia, including COVID-19 related cases.
This AI-driven approach can support clinical decision-making and improve patient outcomes.