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--- |
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license: apache-2.0 |
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datasets: brain-tumor-image-dataset-semantic-segmentation |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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pipeline_tag: image-classification |
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tags: |
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- brain-tumor |
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- image-classification |
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- keras |
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- tensorflow |
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- cnn |
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- mri |
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- healthcare |
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--- |
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# Tumor Detection ML Model |
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## Model Description |
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This model is designed to classify brain tumor images using a Convolutional Neural Network (CNN). It has been trained and fine-tuned on a labeled dataset of brain tumor MRI images. |
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## Training Details |
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- **Framework:** TensorFlow/Keras |
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- **Optimizer:** Adam with a learning rate scheduler |
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- **Loss Function:** Categorical Crossentropy |
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- **Data Augmentation:** Includes rotation, width/height shift, zoom, and horizontal flipping. |
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- **Hyperparameter Tuning:** Performed using Keras Tuner. |
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## Metrics |
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The following metrics were used to evaluate the model's performance: |
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- **Accuracy:** Measures the overall correctness of predictions. |
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- **F1 Score:** Balances precision and recall. |
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- **Precision:** Indicates the proportion of true positives among positive predictions. |
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- **Recall:** Indicates the proportion of true positives among all actual positives. |
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## Usage |
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You can load the model using the Hugging Face Transformers library: |
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```python |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model") |
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