Instructions to use Abdulhaque/breast-cancer-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Abdulhaque/breast-cancer-detection with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Abdulhaque/breast-cancer-detection") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Breast Cancer Detection Model
Model Info
- Architecture: DenseNet121 + Custom Head
- Task: Binary Classification (Cancer vs No Cancer)
- Dataset: IDC Breast Histopathology (277,524 images)
- Input Size: 96x96x3
Performance
| Metric | Value |
|---|---|
| AUC-ROC | 0.9432 |
| Sensitivity | 94.64% |
| Specificity | 76.13% |
| F1 Score | 0.78 |
Usage
import tensorflow as tf
model = tf.keras.models.load_model("best_model_ft.keras")
Threshold
Use threshold 0.3 for hospital deployment (maximize sensitivity).
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