Brain Tumor Detection

This repository contains a Jupyter Notebook for detecting brain tumors using machine learning techniques. The project involves preprocessing medical images, training a model, and evaluating its performance on detecting tumors.

Project Overview

The Brain Tumor Detection project includes the following key components:

• Data Preparation: Organizing and preprocessing medical images for model training.
• Model Training: Utilizing deep learning techniques to train a model on the preprocessed images.
• Evaluation: Assessing the model's performance on a separate test dataset to evaluate its accuracy in detecting brain tumors.

Installation

To run this project locally, you will need to have Python 3 installed along with the following libraries:

• numpy
• opencv-python
• tensorflow or keras
• matplotlib
• sklearn
• tqdm
• imutils

You can install these dependencies using pip:

pip install numpy opencv-python tensorflow matplotlib scikit-learn tqdm imutils

Usage

1. Clone the Repository:

   git clone https://github.com/precioux/brain-tumor-detection.git
   cd brain-tumor-detection

2. Open the Jupyter Notebook:

   Launch Jupyter Notebook and open the brain-tumor-detection.ipynb file.

   jupyter notebook brain-tumor-detection.ipynb

3. Run the Cells:

   Execute the cells in the notebook sequentially to load the data, preprocess it, train the model, and evaluate its performance.

4. Evaluating the Model:

   The notebook includes code for visualizing the results, including sample predictions and model accuracy metrics.

Data

The project utilizes a dataset of brain MRI images labeled as either "tumor" or "no tumor". The data is organized into training, validation, and test sets to ensure robust model evaluation.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• The project is inspired by the need for efficient and accurate medical diagnosis using AI.
• Special thanks to the creators of the dataset and the authors of the libraries used in this project.
• Dataset: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection