A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection

Area of Science:

• Medical Imaging and Artificial Intelligence
• Infectious Disease Diagnostics
• Deep Learning Applications in Healthcare

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, presents significant global health challenges, including high mortality and morbidity.
• Radiographic features in chest X-rays are crucial for identifying COVID-19, but manual interpretation by radiologists faces limitations.
• Variations in viral types and symptoms complicate diagnosis and treatment strategies.

Purpose of the Study:

• To develop and evaluate a hybrid deep learning model for accurate COVID-19 diagnosis using chest X-ray images.
• To specifically address the challenges in diagnosing the Delta variant of COVID-19.
• To assess the model's capability for severity-based analysis of infected patients.

Main Methods:

• A hybrid deep learning model integrating Visual Geometry Group 16 (VGG16) and Support Vector Machine (SVM) was proposed.
• VGG16 was utilized for the primary identification of COVID-19 infection from X-ray images.
• SVM was employed for subsequent severity-based analysis of the diagnosed cases.

Main Results:

• The hybrid VGG16-SVM model achieved a high overall accuracy rate of 97.37% in diagnosing COVID-19 from X-ray images.
• Performance was further validated using metrics including AUC, precision, F-score, misclassification rate, and confusion matrix.
• The model demonstrated significant applicability and effectiveness compared to other relevant diagnostic techniques.

Conclusions:

• The proposed hybrid deep learning model offers a highly accurate and efficient tool for the diagnosis of COVID-19, particularly the Delta variant.
• The model's ability to perform both identification and severity assessment enhances its clinical utility.
• This research highlights the potential of AI-driven solutions in overcoming bottlenecks in radiological diagnosis for infectious diseases.