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Updated: Jun 15, 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

COVID-19 severity detection using chest X-ray segmentation and deep learning.

Tinku Singh¹, Suryanshi Mishra², Riya Kalra³

¹School of Information and Communication Engineering, Chungbuk National University, Cheongju, South Korea.

Scientific Reports

|August 27, 2024

Summary

This summary is machine-generated.

This study presents a deep learning framework using chest radiographs (CXR) for accurate COVID-19 detection and severity assessment. The AI model aids in early diagnosis and patient management, improving clinical decision-making.

Keywords:

Brixia score COVID-19 Capsule network Chest X-ray Deep learning

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Published on: June 21, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

COVID-19 significantly impacts global health and daily life, necessitating efficient diagnostic tools.
Chest radiographs (CXR) offer a practical alternative to CT scans for COVID-19 diagnosis, despite potential sensitivity limitations.
Accurate classification and severity prediction are crucial for effective patient management and resource allocation.

Purpose of the Study:

To develop and validate a deep learning framework for COVID-19 classification and severity prediction using CXR images.
To enhance the diagnostic accuracy of CXR for COVID-19 detection.
To improve the assessment of COVID-19 severity for better clinical outcomes.

Main Methods:

A deep learning framework integrating U-Net for lung segmentation, a Convolution-capsule network for classification, and ResNet50, VGG-16, and DenseNet201 for severity assessment.
U-Net achieved a lung segmentation precision of 0.9924.
The classification model demonstrated high true positive rates: 86% for COVID-19, 93% for pneumonia, and 85% for normal cases.

Main Results:

DenseNet201 exhibited superior accuracy in COVID-19 severity assessment compared to ResNet50 and VGG-16.
The framework achieved high classification performance, with validated results using 95% confidence intervals.
The integrated deep learning approach proved reliable and robust for analyzing CXR images.

Conclusions:

The developed deep learning framework effectively classifies COVID-19 and predicts disease severity from CXR images.
This AI-driven approach enhances early detection and assessment of COVID-19, supporting improved patient care.
The study highlights the potential of integrating advanced AI techniques with radiological imaging for clinical decision support.