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Brain Tumor Detection

This project is aimed at detecting brain tumors using a convolutional neural network (CNN) based on the VGG19 architecture. The model is integrated into a Flask web application to provide a user-friendly interface for uploading and analyzing MRI images. alt text

Table of Contents

Introduction

Brain tumor detection is a critical task in the medical field. This project utilizes deep learning techniques to classify MRI images as either "Brain Tumor" or "No Brain Tumor". The trained model is deployed in a Flask web application for easy accessibility and use.

Dataset

The dataset used for this project consists of MRI images categorized into two classes: "Yes" for images with brain tumors and "No" for images without brain tumors. The dataset is preprocessed and augmented to ensure robust model training.

Model Architecture

The model is based on the VGG19 architecture, a popular deep learning model known for its effectiveness in image classification tasks. The base model is fine-tuned with additional dense layers to improve performance for this specific task.

Setup

Prerequisites

  • Python 3.7+
  • TensorFlow 2.x
  • Flask
  • OpenCV
  • NumPy
  • PIL

Installation

  1. Clone the repository:

    git clone https://github.com/shikharrajat/BrainTumor-Detection.git
cd BrainTumor-Detection

  2. Install the required packages:

    pip install -r requirements.txt

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

  4. Ensure the model weights file vgg_unfrozen.h5 is in the project directory.

Usage

  1. Run the Flask application:

    python app.py

  2. Open your web browser and go to http://127.0.0.1:5000/.

  3. Upload an MRI image to get the prediction result.

Results

The model achieves high accuracy in detecting brain tumors from MRI images. Example results and performance metrics will be provided here.

Contributing

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