https://github.com/guygav/aies-ccp-project-brain-tumor-detection
Detect brain tumors from MRI scans using CNN and VGG19 with a Streamlit-based user interface.
https://github.com/guygav/aies-ccp-project-brain-tumor-detection
ai-in-healthcare brain-tumor-detection cnn computer-vision deep-learning image-classification keras medical-imaging mri-images neural-networks open-cv python streamlit tensorflow transfer-learning tumor-detection vgg16
Last synced: 2 months ago
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Detect brain tumors from MRI scans using CNN and VGG19 with a Streamlit-based user interface.
- Host: GitHub
- URL: https://github.com/guygav/aies-ccp-project-brain-tumor-detection
- Owner: guygav
- License: mit
- Created: 2025-05-21T18:37:00.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-07-17T21:03:42.000Z (3 months ago)
- Last Synced: 2025-07-17T22:19:47.379Z (3 months ago)
- Topics: ai-in-healthcare, brain-tumor-detection, cnn, computer-vision, deep-learning, image-classification, keras, medical-imaging, mri-images, neural-networks, open-cv, python, streamlit, tensorflow, transfer-learning, tumor-detection, vgg16
- Language: Jupyter Notebook
- Size: 20.4 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠AIES-CCP Project: Brain Tumor Detection
Welcome to the **AIES-CCP Project: Brain Tumor Detection** repository! This project aims to help detect brain tumors from MRI scans using advanced machine learning techniques, specifically Convolutional Neural Networks (CNN) and VGG19. The project features a user-friendly interface built with Streamlit, making it accessible for both medical professionals and researchers.
## Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Technologies Used](#technologies-used)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Model Training](#model-training)
7. [Contributing](#contributing)
8. [License](#license)
9. [Contact](#contact)
10. [Releases](#releases)
## Introduction
Brain tumors are a significant health concern, affecting thousands of people worldwide. Early detection can greatly improve treatment outcomes. This project leverages the power of deep learning to analyze MRI images and identify potential tumors. By using CNN and VGG19, we aim to provide a robust solution that can assist in medical imaging diagnostics.
## Features
- **MRI Image Analysis**: The model can analyze MRI scans to detect tumors.
- **User Interface**: A Streamlit-based interface allows users to upload images and view results easily.
- **High Accuracy**: The use of transfer learning with VGG19 enhances detection accuracy.
- **Open Source**: This project is open for contributions and improvements.
## Technologies Used
- **AI in Healthcare**: Utilizing artificial intelligence to improve health outcomes.
- **Convolutional Neural Networks (CNN)**: A deep learning technique for image classification.
- **VGG19**: A popular CNN architecture known for its performance in image recognition tasks.
- **Streamlit**: A framework for building web applications for machine learning projects.
- **Keras**: A high-level neural networks API for building and training models.
- **TensorFlow**: An open-source platform for machine learning.
- **OpenCV**: A library for computer vision tasks.
- **Python**: The primary programming language used in this project.
## Installation
To get started with this project, follow these steps:
1. **Clone the Repository**: Open your terminal and run:
```bash
git clone https://github.com/guygav/AIES-CCP-Project-Brain-Tumor-Detection.git
```
2. **Navigate to the Project Directory**:
```bash
cd AIES-CCP-Project-Brain-Tumor-Detection
```
3. **Install Required Packages**: Use pip to install the necessary libraries:
```bash
pip install -r requirements.txt
```
## Usage
To use the application, follow these steps:
1. **Run the Streamlit App**: In your terminal, execute:
```bash
streamlit run app.py
```
2. **Upload an MRI Image**: Open your web browser and navigate to the local server URL provided in the terminal.
3. **View Results**: After uploading, the model will process the image and display the results, indicating whether a tumor is detected.
## Model Training
If you want to train the model on your dataset, follow these steps:
1. **Prepare Your Dataset**: Ensure your images are organized in a directory structure suitable for training. Typically, this includes separate folders for images with tumors and without tumors.
2. **Modify Configuration**: Adjust the training parameters in `config.py` as needed.
3. **Run Training Script**: Execute the training script:
```bash
python train.py
```
4. **Monitor Training**: You can monitor the training process through the logs generated in the terminal.
## Contributing
We welcome contributions from the community! To contribute:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your branch and create a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
## Contact
For any questions or suggestions, feel free to reach out:
- **Email**: [your-email@example.com](mailto:your-email@example.com)
- **GitHub**: [your-github-profile](https://github.com/your-github-profile)
## Releases
To download the latest release, visit the [Releases section](https://github.com/guygav/AIES-CCP-Project-Brain-Tumor-Detection/releases). Make sure to check this section for updates and new features.
---
This project aims to bridge the gap between technology and healthcare, making brain tumor detection more efficient and accessible. Your support and contributions can make a difference in this important field. Thank you for being a part of this journey!