A robust ensemble-deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

Keyphrases

• Computed Tomography 100%
• Image Database 100%
• Scanned Image 100%
• Ensemble Deep Learning Model 100%
• COVID-19 Diagnosis 100%
• COVID-19 75%
• Deep Network 50%
• Label Noise 50%
• Performance Improvement 25%
• Mortality Rate 25%
• Artificial Intelligence 25%
• Open Dataset 25%
• Deep Learning Model 25%
• Feature Extracting 25%
• Early Diagnosis 25%
• Ensemble Model 25%
• Infection Rate 25%
• Contagious Diseases 25%
• Computer-aided Diagnosis (CADx) 25%
• Interobserver 25%
• Interobserver Variability 25%
• Robust Prediction 25%
• Meta-learner 25%
• COVID-19 Detection 25%
• Global Extent 25%
• Hospital Resources 25%
• Attention-aware 25%
• Residual Attention 25%
• Noisy Labels 25%
• DenseNet Architecture 25%
• Chest CT 25%
• Complementary Attention 25%
• Global Set 25%
• Attention Architecture 25%

Computer Science

• Image Database 100%
• Deep Learning Model 100%
• Artificial Intelligence 33%
• Open Source 33%
• Generalizability 33%
• Medical Diagnosis 33%
• Extracted Feature 33%
• Observer Variability 33%
• Mortality Rate 33%
• DenseNet 33%