Brain Tumor MRI Detection using YOLOv8

This project implements a Brain Tumor MRI Detection system using the YOLOv8 model. The workflow includes data preparation, model training, and deployment through a Tkinter application and a Flask web application. The entire process is documented in a Jupyter Notebook for ease of understanding and reproducibility.

Table of Contents

• Introduction
• Dataset
• Requirements
• Project Structure
• Installation
• Usage
  • Training the Model
  • Deploying the Model
    • Tkinter Application
    • Flask Web Application
• Contributing
• License

Introduction

Brain tumors are a critical health issue, and early detection is crucial for effective treatment. This project leverages the YOLOv8 model, a state-of-the-art object detection algorithm, to identify brain tumors from MRI scans. The project is implemented in Python and provides both a desktop and web interface for users to interact with the model.

Dataset

The dataset used for this project consists of MRI scans labeled for the presence of brain tumors. You can download the dataset from Kaggle or any other open-source medical image repository. Ensure that the dataset is organized in a format compatible with YOLOv8 training requirements. The dataset I personally used : My dataset

Requirements

• Python 3.7+
• Jupyter Notebook
• TensorFlow or PyTorch (depending on the YOLOv8 implementation)
• OpenCV
• Tkinter
• Flask
• Other dependencies specified in requirements.txt

Project Structure

Brain-Tumor-Detection/ ├── dataset/ # Directory containing the MRI dataset ├── brain_tumor_detection.ipynb ├── tkinter.py ├── webapp/ # Flask web application code │ ├── static/ │ ├── templates/ ├── app.py ├── requirements.txt # Python dependencies └── README.md # Project documentation

Installation

Clone the repository

git clone https://github.com/aPassie/Brain_Tumor_MRI_modeltest11.git
cd Brain-Tumor-Detection

Install the required packages

pip install -r requirements.txt

Usage

Training the Model

1. Open the Jupyter Notebook:

jupyter notebook final2/Brain_Tumor_MRI_Detection.ipynb

2. Follow the instructions in the notebook to preprocess the data, configure the YOLOv8 model, and train it using the dataset.

Deploying the Model

Tkinter Application

1. Run the Tkinter application:

python tkinter.py

2. Use the GUI to upload MRI images and get predictions.

Flask Web Application

1. Start the Flask server:

python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000 to use the web interface.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

License

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