metadata
datasets:
- ctmedtech/DDR-dataset
library_name: keras
Diabetic Retinopathy Classifiers
Model for detecting Diabetic Retinopathy from retinal fundus images.
Trained as part of the paper: Automated and Explainable Detection of Multiple Diseases from Retinal Fundus Images
Model Architectures
Loading any one model
from keras.models import model_from_json
import json
model = keras.Model.from_config(config)
model.load_weights("model.weights.h5")
with open("deep_learning/ResNet50_enhanced/ResNet50_pretrained_2000_enhanced.json", 'r') as json_file:
model_json = json_file.read()
model = model_from_json(model_json)
model.load_weights("deep_learning/ResNet50_enhanced/ResNet50_pretrained_2000_enhanced.weights.h5")
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Best Performance (Enhanced ResNet50 backbone)
Presence
- Accuracy: 93.4%
- Precision: 92.34%
- Recall: 94.6%
Grading
- Accuracy: 79.35%
- Precision: 79.15%
- Recall: 79.35%
- Cohen's Kappa (linear): 82.89%
- Cohen's Kappa (quadratic): 90.67%
Citation
@inproceedings{masti2026automated,
title={Automated and Explainable Detection of Multiple Diseases from Retinal Fundus Images},
author={Masti, Shubha and Prasad, T. and Srinivasa, G.},
booktitle={Image Processing and Vision Engineering. IMPROVE 2025},
series={Communications in Computer and Information Science},
volume={2628},
publisher={Springer},
year={2026},
doi={10.1007/978-3-032-01169-5_9}
}