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Updated: Aug 24, 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

BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.

Zili Cao¹, Junjian Huang¹, Xing He¹

¹Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China.

Knowledge-Based Systems

|October 26, 2022

Summary

A new method, BND-VGG-19, enhances COVID-19 detection using X-ray images. This approach achieves 95.48% accuracy, improving upon existing diagnostic techniques for faster identification of the virus.

Keywords:

COVID-19 Classification Diagnosis VGG-19 X-ray image

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Last Updated: Aug 24, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

The COVID-19 pandemic presents a significant global health challenge, overwhelming healthcare systems.
Rapid and accurate detection of COVID-19 is crucial for effective patient management and containment.
Existing diagnostic methods face challenges in speed and efficiency, necessitating technological advancements.

Purpose of the Study:

To develop an advanced method for accurate and rapid COVID-19 detection.
To improve the efficiency of virus detection using modern technology and visual assistance.
To introduce the BND-VGG-19 method for enhanced diagnostic capabilities.

Main Methods:

The BND-VGG-19 method, an enhanced VGG-19 architecture, was proposed.
Batch normalization and dropout layers were integrated into the VGG-19 model to boost network accuracy.
A dataset comprising COVID-19, viral pneumonia, and normal X-ray images was utilized for training and testing.

Main Results:

The BND-VGG-19 method demonstrated superior performance in diagnosing lung abnormalities.
The algorithm achieved a high accuracy rate of 95.48% in identifying COVID-19 cases.
Comparative analysis showed the proposed method outperforming existing COVID-19 diagnostic techniques.

Conclusions:

The BND-VGG-19 method offers a highly accurate and efficient approach for COVID-19 detection.
Integrating batch normalization and dropout layers significantly enhances the diagnostic accuracy of deep learning models.
This AI-driven approach holds promise for improving healthcare responses to infectious disease outbreaks.