Brain Tumor Detection Using Deep Learning

Overview

This project implements a deep learning-based solution for brain tumor detection from MRI scans. The system uses advanced computer vision and deep learning techniques to analyze medical images and classify them as either containing a tumor or being tumor-free.

Features

• Deep learning-based brain tumor detection
• MRI scan analysis
• High accuracy classification
• User-friendly interface
• Support for various image formats

Project Structure

Brain_Tumor_Detection/
├── Dataset/           # Contains the MRI scan dataset
├── Notebook/         # Jupyter notebooks for model development and analysis
└── Brain Tumor Detection Using Deep Learning.pdf  # Project documentation

Requirements

• Python 3.x
• TensorFlow/Keras
• OpenCV
• NumPy
• Pandas
• Matplotlib
• scikit-learn

Setup Instructions

1. Clone the repository:

git clone https://github.com/Gmehta604/Brain_Tumor_Detection.git
cd Brain_Tumor_Detection

2. Install the required dependencies:

pip install -r requirements.txt

3. Download the dataset and place it in the Dataset directory

4. Run the Jupyter notebooks in the Notebook directory to train and test the model

Usage

1. Open the Jupyter notebooks in the Notebook directory
2. Follow the instructions in the notebooks to:
   • Preprocess the MRI images
   • Train the deep learning model
   • Evaluate the model performance
   • Make predictions on new images

Model Architecture

The project uses a Convolutional Neural Network (CNN) architecture specifically designed for medical image analysis. The model includes:

• Multiple convolutional layers
• Pooling layers
• Dropout layers for regularization
• Dense layers for classification

Results

The model achieves high accuracy in detecting brain tumors from MRI scans. Detailed performance metrics and results can be found in the project documentation.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Dataset providers
• Research papers and resources that inspired this project
• Open-source community for various tools and libraries used

Contact

For any questions or suggestions, please open an issue in the repository.