Instructions to use shubhamasti/glaucoma-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shubhamasti/glaucoma-models with Keras:
# 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") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - realslimman/REFUGE-MultiRater | |
| library_name: keras | |
| # 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](https://doi.org/10.1007/978-3-032-01169-5_9) | |
| ## 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("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 | |
| ```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} | |
| } | |
| ``` |