Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection

Computer Science

• Classification Accuracy 50%
• Classification Models 50%
• Convolution 100%
• Convolutional Neural Network 100%
• Deep Learning Model 100%
• Feature Extraction 100%
• Inception V3 50%
• Individual End 50%
• Neural Network Model 50%
• Representation Learning 50%
• Residual Neural Network 50%
• Texture Feature 50%
• Texture Information 50%
• Transfer Learning 50%
• Transformer Model 50%
• Vision Transformer 100%
• Weighted Average 50%

Keyphrases

• Convolution-Transformer 100%
• Detector Response 33%
• Disease Classifier 33%
• ViT Model 33%