Paper#Medical Imaging, Deep Learning, CNN, Diabetic Retinopathy🔬 ResearchAnalyzed: Jan 3, 2026 23:58
CNN Fusion for Diabetic Retinopathy Screening
Published:Dec 26, 2025 04:54
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for efficient and accurate diabetic retinopathy (DR) screening, a leading cause of preventable blindness. It explores the use of feature-level fusion of pre-trained CNN models to improve performance on a binary classification task using a diverse dataset of fundus images. The study's focus on balancing accuracy and efficiency is particularly relevant for real-world applications where both factors are crucial for scalability and deployment.
Key Takeaways
- •Feature-level fusion of CNN backbones improves DR screening accuracy compared to single models.
- •The Eff+Den fusion model provides a good balance between accuracy and computational efficiency.
- •Lightweight fusion models can generalize well across heterogeneous datasets.
- •The study highlights the importance of considering both accuracy and throughput in real-world DR screening workflows.
Reference
“The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89%) with balanced class-wise F1-scores.”