Brain MRI Classification & Segmentation 🧠🩺

🚀 Project Overview

This notebook provides an end-to-end pipeline for two tasks on Brain MRI scans:

• Classification: Distinguish between healthy and diseased MRI images using Transfer Learning (ResNet50).
• Segmentation: Delineate regions of interest (e.g., tumors) in MRI slices with U-Net architectures.

All code (data loading, EDA, model training, evaluation, and visualization) is contained within a single Jupyter notebook.

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📂 Files

├── Brain_MRI_Classification&Segmentation.ipynb   # Comprehensive notebook with code & plots
├── images/                                      # Visualization outputs (PNG files)
├── requirements.txt                             # Python dependencies
└── README.md                                    # Project overview and instructions

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🔗 Data Reference

This project uses the Brain MRI segmentation dataset on Kaggle.

1. Visit the Kaggle link above.
2. Download and unzip the dataset.

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/v4nui/Brain_MRI_Class-Seg.git
   cd brain-mri-analysis

2. Create & activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate   # Linux/macOS
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

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🔍 Usage

Open and run the notebook:

jupyter notebook Brain_MRI_Classification\&Segmentation.ipynb

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🤝 Contributing

Contributions and suggestions are welcome! Please open an issue or submit a pull request.

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📬 Contact

For questions or feedback, reach out at vanuhi@live.com.