Instructions to use Bharathikannan/brain-tumor-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Bharathikannan/brain-tumor-classification with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Bharathikannan/brain-tumor-classification") - Notebooks
- Google Colab
- Kaggle
NeuroLens โ brain tumor MRI classifier
Selected model of the NeuroLens project (NUS-ISS PRS Practice Module): Fibonacci-Net (lr=0.001).
- Classes (output order): glioma, meningioma, notumor, pituitary
- Input: 224 x 224 RGB, pixels / 255
- Explanation method: Grad-CAM
- Test metrics: accuracy 0.972, macro_f1 0.971
- Training data: Kaggle Brain Tumor MRI dataset (masoudnickparvar, version 1)
Research prototype โ not a diagnostic device. Full details in model_info.json.
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