Search:
Match:
2 results
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.
Reference

“The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89%) with balanced class-wise F1-scores.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 00:16

FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification

Published:Dec 24, 2025 05:00
1 min read
ArXiv AI

Analysis

This ArXiv paper introduces FGDCC, a novel method to address intra-class variability in Fine-Grained Visual Categorization (FGVC) tasks, specifically in plant classification. The core idea is to leverage classification performance by learning fine-grained features through class-wise cluster assignments. By clustering each class individually, the method aims to discover pseudo-labels that encode the degree of similarity between images, which are then used in a hierarchical classification process. While initial experiments on the PlantNet300k dataset show promising results and achieve state-of-the-art performance, the authors acknowledge that further optimization is needed to fully demonstrate the method's effectiveness. The availability of the code on GitHub facilitates reproducibility and further research in this area. The paper highlights the potential of cluster-based approaches for mitigating intra-class variability in FGVC.
Reference

“Our goal is to apply clustering over each class individually, which can allow to discover pseudo-labels that encodes a latent degree of similarity between images.”

PermalinkArXiv AI