Automated dual CNN-based feature extraction with SMOTE for imbalanced diabetic retinopathy classification

Engineering

• Convolutional Neural Network 100%
• Based Feature Extraction 100%
• Feature Extraction 100%
• Automated Model 100%
• Metrics 50%
• Classification Accuracy 50%
• Extracted Feature 50%
• Extraction Process 50%
• Multiclass Classification 50%

Computer Science

• Feature Extraction 100%
• Convolutional Neural Network 100%
• Class Imbalance 66%
• Classification Accuracy 33%
• Data Augmentation 33%
• Binary Classification 33%
• Extraction Process 33%
• Residual Neural Network 33%
• Average Pooling 33%
• Extracted Feature 33%
• Multiclass Classification 33%
• Effective Treatment 33%
• Training Data 33%
• ResNet50 33%

Earth and Planetary Sciences

• Pattern Recognition 100%