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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

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning

Abeer Aljohani¹, Nawaf Alharbe¹

¹Computer Science Department, Applied College, Taibah University, Madinah 46537, Saudi Arabia.

Healthcare (Basel, Switzerland)

|December 23, 2022

Summary

This study introduces a computer-assisted diagnosis system for rapid COVID-19 detection using X-ray images. The proposed master-slave architecture with DenseNet201 achieved 83.33% accuracy, outperforming standard methods.

Keywords:

COVID-19 detection chest X-ray image computer-assisted diagnosis transfer learning image classification

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Area of Science:

Medical Imaging and Diagnostics
Artificial Intelligence in Healthcare
Infectious Disease Detection

Background:

Early diagnosis of COVID-19 is crucial for disease management and control.
Traditional PCR tests are time-consuming and can yield inaccurate results.
There is a need for rapid, low-cost diagnostic tools, especially in resource-limited settings.

Purpose of the Study:

To develop a computer-assisted diagnosis (CAD) system for differentiating COVID-19 from healthy and pneumonia cases using X-ray images.
To evaluate the effectiveness of transfer learning techniques for COVID-19 detection in chest X-rays.
To propose an optimized master-slave architecture for improved diagnostic accuracy.

Main Methods:

Utilized transfer learning techniques, specifically DenseNet201 and SqueezeNet1_0, within a master-slave architecture.
Classified COVID-19 cases from chest X-ray image sequences.
Compared the proposed models against standard transfer learning approaches.
Fine-tuned hyperparameters and optimized learning rates for enhanced model performance.

Main Results:

The proposed master-slave architecture using DenseNet201 achieved an accuracy of 83.33%.
SqueezeNet1_0 demonstrated the fastest processing time with an accuracy of 80%.
The developed CAD system outperformed existing standard transfer learning methods for COVID-19 detection.

Conclusions:

Computer-assisted diagnosis systems utilizing X-ray imaging offer a promising alternative for rapid COVID-19 detection.
Transfer learning models, particularly DenseNet201 with optimized parameters, show high potential for accurate COVID-19 classification.
The proposed architecture provides a foundation for developing efficient and accessible diagnostic tools for infectious diseases.