NeuraScan - Brain Tumor Detection App

Overview

NeuraScan is a machine learning-based web application designed to detect and classify brain tumors from MRI scans. This project utilizes a deep learning model that predicts the type of tumor, such as Glioma, Meningioma, Pituitary Tumor, or No Tumor, with a high degree of accuracy. The application provides valuable recommendations based on the analysis, making it an essential tool for early detection and medical consultation.

Features

• User-Friendly Interface: A sleek, modern UI with an easy-to-use file upload mechanism.
• Accurate Predictions: The app leverages a trained machine learning model to classify brain tumors.
• Recommendations: Provides tailored next steps and recommendations based on the prediction result.
• Tumor Information: Offers detailed information about different types of brain tumors.

Technologies Used

• Frontend: HTML, CSS (Bootstrap), JavaScript (for file handling and UI)
• Backend: Python (Flask)
• Machine Learning: Trained deep learning model (TensorFlow/Keras)
• File Handling: Uploads MRI scans in various formats (JPEG, PNG)
• Deployment: Works locally or can be deployed to any cloud provider (Heroku, AWS, etc.)

Installation and Setup

To set up the project locally, follow these steps:

Prerequisites

• Python 3.x
• Flask
• TensorFlow/Keras
• Jupyter (Optional, for model testing)
• Git (for cloning the repository)

Steps

1. Clone the Repository

   First, clone the project repository from GitHub:

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

2. Set Up a Virtual Environment (Optional but Recommended)

   It's a good practice to use a virtual environment to isolate dependencies:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

   Install all necessary Python packages:

   pip install -r requirements.txt

4. Download the Trained Model

   You need to place the trained deep learning model in the root directory. If you haven't trained a model yet, you can use a pre-trained one (optional). For this project, make sure the model file is named brain_tumor_detection_model.keras and placed in the root directory.

5. Set Up Flask Environment Variables

   Create a .env file in the root directory and define your Flask environment variables:

   FLASK_APP=app.py
   FLASK_ENV=development

6. Run the Application

   Use Flask to start the server:

   flask run

7. Access the Application

   Once the server is running, open your browser and go to:

   http://127.0.0.1:5000
   

   From there, you can upload an MRI image and get predictions.

Project Directory Structure

brain-tumor-detection/
├── app.py               # Main Flask application file
├── model/               # Directory containing the trained model
│   └── brain_tumor_detection_model.keras    # Pre-trained model for prediction
├── static/              # Static files (CSS, JS, Images)
├── templates/           # HTML templates for Flask views
│   ├── index.html        # Homepage for file upload
│   └── result.html       # Analysis result page
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation (this file)

Running the Model

The app uses a trained machine learning model to predict the type of tumor from an MRI image. If you have your own model, ensure it's compatible and place it in the root directory.

Prediction Types

The app classifies brain MRI scans into four categories:

• Glioma: Aggressive tumors originating from glial cells.
• Meningioma: Typically benign tumors arising from the meninges.
• Pituitary Tumor: Benign tumors near the pituitary gland.
• No Tumor: Indicates no detectable brain tumor in the MRI scan.

Model Performance Metrics

Accuracy Graph

Loss Graph

Confusion Matrix

Future Improvements

Some ideas for improving this project include:

• Implementing a more detailed analysis report.
• Allowing multiple file uploads for batch processing.
• Improving the model's accuracy with a larger dataset.
• Deploying the application on a cloud platform.

NeuraScan - Enhancing brain tumor diagnosis through machine learning.