A bagging dynamic deep learning network for diagnosing COVID-19.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Deep Learning

Background:

• COVID-19 remains a significant global health threat.
• Accurate and rapid diagnosis is crucial for patient management.
• Chest X-ray radiography offers a convenient imaging modality for COVID-19 detection.

Purpose of the Study:

• To develop an automated system for COVID-19 diagnosis using chest X-ray images.
• To propose a novel deep learning model for enhanced diagnostic accuracy.
• To provide a tool for clinical decision support in COVID-19 diagnosis.

Main Methods:

• Image preprocessing techniques were applied to chest X-ray datasets.
• Convolution blocks were pre-trained as feature extractors.
• A bagging dynamic learning network classifier was developed using neural dynamic and bagging algorithms.

Main Results:

• The proposed bagging dynamic deep learning network (B-DDLN) achieved a testing accuracy of 98.8889%.
• B-DDLN demonstrated superior diagnostic performance compared to existing state-of-the-art methods.
• The model effectively recognized COVID-19 symptoms in X-ray images.

Conclusions:

• The B-DDLN model offers a highly accurate and efficient method for automated COVID-19 diagnosis from chest X-rays.
• This AI-driven approach can significantly aid clinicians in diagnosis and treatment planning.
• The findings support the integration of deep learning in medical imaging for pandemic response.