Brain Tumor Detection

Overview

This project focuses on brain tumor detection using deep learning techniques. The provided Jupyter Notebook (brain-tumor-detection.ipynb) implements a model that classifies brain MRI images as tumorous or non-tumorous. The goal is to assist in early detection and diagnosis.

Features

• Uses Convolutional Neural Networks (CNN) for classification.
• Trained on a dataset of brain MRI images.
• Performs image preprocessing including resizing, normalization, and augmentation.
• Provides accuracy metrics and visualizations.

Installation

To run this project, install the required dependencies:

pip install -r requirements.txt

If a requirements.txt file is not available, manually install the necessary libraries:

pip install numpy pandas matplotlib seaborn tensorflow keras opencv-python scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/sharmaniraj009/Brain-Tumor-Detection.git
   cd Brain-Tumor-Detection

2. Open the Jupyter Notebook:

   jupyter notebook brain-tumor-detection.ipynb

3. Run all the cells sequentially to train and test the model.

Dataset

• The dataset consists of brain MRI images categorized as having tumors or being normal.
• If the dataset is not included, download it from a publicly available source and update the file paths accordingly.

Model Architecture

• CNN-based architecture with convolutional, pooling, and dense layers.
• Activation functions: ReLU and Softmax.
• Loss function: Categorical Cross-Entropy.
• Optimizer: Adam.

Results

• Displays accuracy and loss graphs.
• Confusion matrix and classification report for performance evaluation.

Contributions

Contributions are welcome! Feel free to open issues or submit pull requests.

Contact

For questions or feedback, reach out via GitHub issues or the repository owner.

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Note: Ensure that the dataset is correctly linked before running the notebook.