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A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray

Soufiane Hamida¹, Oussama El Gannour¹, Bouchaib Cherradi^1,2

¹SSDIA Laboratory, ENSET of Mohammedia, Hassan II University of Casablanca, BP 159, Mohammedia, Morocco.

Journal of Healthcare Engineering

|November 15, 2021

Summary

This study developed a rapid COVID-19 diagnosis system using chest X-rays and AI. The system achieved 99.23% accuracy, outperforming previous methods for detecting infectious respiratory diseases.

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Infectious Disease Diagnostics

Background:

COVID-19 presents with flu-like symptoms and can cause pneumonia, necessitating rapid diagnostic tools.
Chest X-rays are crucial for diagnosing respiratory conditions, including COVID-19.
Accurate and swift detection of COVID-19 is vital for patient management and public health.

Purpose of the Study:

To develop a rapid and accurate AI-powered system for COVID-19 detection from chest X-ray images.
To leverage transfer learning and stacking ensemble methods for enhanced diagnostic performance.
To create a robust model capable of differentiating COVID-19 from other respiratory conditions.

Main Methods:

Utilized transfer learning with pre-trained convolutional neural networks to analyze X-ray images.
Implemented a stacking ensemble approach, combining multiple transfer learning models for improved accuracy.
Trained and validated the model on a diverse dataset including COVID-19, tuberculosis, viral pneumonia, and normal cases.

Main Results:

The developed system achieved a high accuracy of 99.23% in detecting COVID-19 from chest X-rays.
The stacking ensemble method significantly improved the diagnostic performance compared to individual models.
The system demonstrated robustness in classifying various respiratory conditions.

Conclusions:

The proposed AI system offers a highly accurate and rapid method for COVID-19 diagnosis using chest X-rays.
Transfer learning and stacking ensemble techniques are effective for building robust medical image analysis tools.
This approach shows promise for supporting clinical decision-making in diagnosing infectious respiratory diseases.