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--- |
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license: mit |
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datasets: |
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- masoudnickparvar/brain-tumor-mri-dataset |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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library_name: keras |
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tags: |
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- cnn |
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- keras |
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- brain-tumor |
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- medical-imaging |
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- tensor-flow |
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- image-classification |
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language: |
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- en |
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--- |
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Brain Tumor Detection CNN Model |
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This model was trained using a Convolutional Neural Network (CNN) to classify brain MRI images as either having a tumor or not. It uses Keras with TensorFlow backend and was trained on the publicly available [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset) from Kaggle. |
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Dataset |
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The dataset contains 3,762 T1-weighted contrast-enhanced MRI images, labeled as: |
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- **Yes** – Images with a brain tumor |
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- **No** – Images without a brain tumor |
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The data is balanced and preprocessed into two folders: `yes/` and `no/`. |
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Train Accuracy: ~98% |
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Validation Accuracy: ~96% |
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## 🧠 Model Architecture |
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- Type: CNN |
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- Framework: Keras (TensorFlow backend) |
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- Input shape: `(150, 150, 3)` |
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- Final Activation: `sigmoid` |
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- Loss: `binary_crossentropy` |
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- Optimizer: `Adam` |
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Example (simplified): |
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```python |
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model = Sequential([ |
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Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)), |
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MaxPooling2D(2,2), |
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Conv2D(64, (3,3), activation='relu'), |
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MaxPooling2D(2,2), |
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Flatten(), |
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Dense(128, activation='relu'), |
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Dense(1, activation='sigmoid') |
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]) |
