🧠 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.

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📌 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)

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🎯 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

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🏋️ 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)

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🧠 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.

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📊 Evaluation

Metrics

• Dice Coefficient
• Intersection over Union (IoU)

Example Results