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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 - kush3489t@gmail.com