arXiv (All)
open access
LightHybridNet: A Lightweight Hybrid Deep Learning Framework for Efficient Multi-Label Ophthalmic Disease Classification in Imbalanced Fundus Images
Eye diseases are a major global cause of blindness and vision impairment, highlighting the need for accurate and early diagnosis to prevent further deterioration. Despite advances in medical imaging, classifying retinal diseases from fundus images remains challenging due to the complexity and subtle
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.#476838
- Advancements in AI-Enhanced OCT Imaging for Early Disease Detection and Prevention in Aging Populations#73595
- Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma#310946
- Advanced retinal disease detection using RHT-Net: a hybrid deep learning approach with augmented fundus imaging.#354665
- IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection#1052211
Record · ID 1355660
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive