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A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis
IEEE Transactions on Neural Networks and Learning Systems
|September 2, 2022
Summary
A novel deep ensemble dynamic learning network accurately identifies COVID-19 from chest X-rays. This advanced AI model achieves 98.7% accuracy, aiding rapid diagnosis of coronavirus disease 2019.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer-Aided Diagnosis
Background:
- The COVID-19 pandemic poses a significant global health threat.
- Accurate and rapid diagnosis of COVID-19 is crucial for patient management and disease control.
- Chest X-ray radiography is a widely used imaging modality for diagnosing pneumonia.
Purpose of the Study:
- To develop an intelligent system for automated COVID-19 detection from chest X-ray images.
- To differentiate COVID-19 from other types of pneumonia and normal cases.
- To improve diagnostic efficiency for clinicians.
Main Methods:
- A deep ensemble dynamic learning network was proposed.
- Image preprocessing and dataset division were performed.
- Convolution blocks and pooling layers were used as feature extractors.
- A two-stage bagging dynamic learning network was trained for classification.
Main Results:
- The proposed deep ensemble dynamic learning network achieved a diagnosis accuracy of 98.7179%.
- The model demonstrated superior performance compared to existing state-of-the-art models.
- The system effectively diagnosed the presence and types of pneumonia.
Conclusions:
- The developed deep ensemble dynamic learning network shows high accuracy in diagnosing COVID-19 from chest X-rays.
- This AI-driven approach offers a promising tool for clinical decision support.
- The findings support the use of advanced AI for pandemic detection and management.
