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| license: mit |
| library_name: keras |
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| # 🧠 Unet-Brain-Segmentation |
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| A deep learning-based medical image segmentation model for brain MRI scans, built using a TensorFlow implementation of the U-Net architecture. |
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| ## 📌 Model Overview |
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| This model performs **semantic segmentation** on brain MRI images to identify regions such as tumors or anatomical structures. It is based on the **U-Net architecture**, a widely used convolutional neural network for biomedical image segmentation. |
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| ### Key Details |
| - **Model Type:** Image Segmentation (Semantic Segmentation) |
| - **Architecture:** U-Net |
| - **Framework:** TensorFlow / Keras |
| - **Domain:** Medical Imaging (Brain MRI) |
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| ## 🎯 Intended Use |
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| ### ✅ Primary Use |
| - Automatic segmentation of brain MRI images |
| - Research in medical imaging and deep learning |
| - Educational and experimental purposes |
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| ### ❌ Out-of-Scope Use |
| - Not intended for clinical diagnosis |
| - Should not be used for real-world medical decisions without professional validation |
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| ## 🏋️ Training Details |
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| ### Dataset |
| - Brain MRI dataset with corresponding segmentation masks |
| *(Specify dataset if available, e.g., BraTS or Kaggle Brain MRI dataset)* |
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| ### Preprocessing |
| - Image resizing (e.g., 128×128 or 224×224) |
| - Normalization |
| - Optional data augmentation (rotations, flips, etc.) |
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| ### Training Configuration |
| - **Loss Function:** Dice Loss / Binary Cross-Entropy *(update accordingly)* |
| - **Optimizer:** Adam |
| - **Batch Size:** *(add your value)* |
| - **Epochs:** *(add your value)* |
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| ## 🧠 Model Architecture |
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| The model follows the classic **U-Net encoder–decoder structure**: |
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| - **Encoder:** Extracts hierarchical features from input images |
| - **Decoder:** Upsamples features to generate segmentation masks |
| - **Skip Connections:** Preserve spatial information and improve localization |
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| This design enables **precise pixel-level predictions**, which are essential for medical image analysis. |
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| ## 📊 Evaluation |
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| ### Metrics |
| - Dice Coefficient |
| - Intersection over Union (IoU) |
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| ### Example Results |
