DREAM-RFI: the Diabetic Retinopathy Enhanced, Adapted, and Merged Retinal Fundus Image Dataset

By Delu, Mukesh, Harjule, Priyanka, Kumar, Rajesh, Gajjar, KushalToolReference information

Resource information

Format: zip
Original repository: 17587015
DOI: 10.5281/zenodo.17587015
Credit basis: Creators in Zenodo record metadata
Access: Open files available from the original Zenodo deposit
Last checked: Nov 5, 2026
License: mit-license
Last record update: Nov 5, 2026

About this resource

DREAM-RFI Dataset v1.0.0 - Initial Publication Release This release accompanies the paper "A Harmonized Multi-Source Dataset with Baseline Deep Learning Validation for Staging Diabetic Retinopathy" submitted to IJMEMS. Dataset Overview Total Images : 3,461 quality-filtered retinal fundus images Classes : 5-class DR severity (No DR: 1256, Mild NPDR: 493, Moderate NPDR: 779, Severe NPDR: 396, PDR: 537) Source Datasets : IDRiD, Messidor-2, SUSTech-SYSU, APTOS, DeepDRiD-v1.1, Zenodo DR V03 Standard Resolution : 1024×1024×3 pixels Grading Standard : ICDR severity scale (0-4) Repository Contents Complete quality assessment pipeline (10-metric framework) Data preprocessing scripts (cropping, resizing, normalization) Dataset-adaptive normalization code Baseline DenseNet-121 training scripts Classification schemes: 2-class, 3-class, 5-class Documentation and usage examples Citation If you use this dataset or code, please cite: [Paper reference will be added upon acceptance] Requirements Python 3.8+ TensorFlow 2.x / PyTorch OpenCV, NumPy, scikit-learn See requirements.txt for full dependencies Contact For questions or issues, please open a GitHub issue or contact -