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