Keras
How to use from the
Use from the
Keras library
# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras

model = keras.saving.load_model("hf://shubhamasti/glaucoma-models")

Glaucoma Classifier

Model for detecting Glaucoma from retinal fundus images.

Trained as part of the paper: Automated and Explainable Detection of Multiple Diseases from Retinal Fundus Images

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("feature_based/CNN_ODOC/CNN_ODOC.json", 'r') as json_file:
    model_json = json_file.read()
model = model_from_json(model_json)
model.load_weights("feature_based/CNN_ODOC/CNN_ODOC.weights.h5")
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Performance

  • Accuracy: 97.38%
  • Precision: 97.42%
  • Recall: 97.38%

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}
}
Downloads last month
-
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Dataset used to train shubhamasti/glaucoma-models