---
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](https://doi.org/10.1007/978-3-032-01169-5_9)

## Model Architectures

![image](https://cdn-uploads.huggingface.co/production/uploads/6a425318cee7260581fab991/TB57u0FVp_yaDkWz4R2dN.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/6a425318cee7260581fab991/ANJU_IAd3s0SsL70UZp50.png)

## Loading any one model

```python
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
```bibtex
@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}
}
```