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Multi-Class Eye Disease: Classification Using Deep Learning Efficientnetb0 Fusion Techniques by alinohririzwan196541 is a document available to read on EtoBox.

What is Multi-Class Eye Disease: Classification Using Deep Learning Efficientnetb0 Fusion Techniques about?

This study presents a dual-backbone deep learning architecture for multi-class eye disease classification, combining EfficientNetB0 with ResNet50, InceptionV3, and AlexNet using various fusion strategies. The model achieved high accuracy rates, with internal validation showing up to 95.26% accuracy and external validation reaching up to 97.99%, demonstrating strong generalization across datasets. The findings suggest that the proposed fusion techniques enhance feature integration and model interpretability,

Author

alinohririzwan196541

Language

EN