Mpox Detector Website

Welcome to the Mpox Detector Website. This project aims to provide a research-based tool for diagnosing Mpox and related diseases using image analysis and survey data. The platform allows users to anonymously upload images of suspected lesions and answer a brief survey regarding symptoms and exposure to Mpox. The collected data helps enhance the model's accuracy and contributes to ongoing research.

Table of Contents

• Overview
• Features
• Model Information
• Author
• Usage
• Installation
• Technologies
• Contributing
• License
• Contact

Overview

This project provides a machine learning-based solution for diagnosing Mpox using images and survey responses. The main goal is to develop a tool that aids in the early detection of Mpox while contributing to research efforts.

The website is hosted using Azure and features a user-friendly interface built with Bootstrap.

Features

• Mpox Diagnosis: Users can upload images of suspected lesions and receive a diagnosis prediction using a machine learning model.
• Symptom Survey: The platform includes a brief survey to gather user-reported symptoms and potential exposure data.
• Disclaimer: A mandatory disclaimer is shown to users before they upload data, outlining the purpose and limitations of the tool.
• Mobile-Responsive Design: The site is optimized for both desktop and mobile users.
• Secure & Anonymous: Data is collected anonymously and securely.

Model Information

• Type of Model: The underlying model is a CNN image classifier trained to detect Mpox.
• Input Data: The model accepts PNG images of skin lesions along with symptom survey data.
• Purpose: The purpose of the model is to aid in the early detection of Mpox for research purposes. It is not intended to replace professional medical advice or diagnosis.

Author

• Name: Jonathan Gong
• Role: Developer, Researcher
• Email: jonathangong2005@gmail.com

Usage

Accessing the Website

You can visit the live site at:

MpoxWeb

Running the Model

1. Agree to the disclaimer to proceed.
2. Fill out the symptom survey.
3. Upload a PNG image of the suspected lesion.
4. Submit the survey and wait for the model to analyze the image.
5. View the prediction results on the same page.

Installation

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/jogong2718/MpoxWeb.git

2. Create an uploads directory and a config.php file:

   • Navigate to the survey folder and create a new directory called uploads (this directory will store the uploaded image data).
   • Create a file named config.php in the survey folder to store your database connection details with the following format:

   <?php
   // Database connection details
   $servername = "####"; // Your database server/host (e.g., localhost)
   $username = "####";   // Your database username
   $password = "####";   // Your database password
   $dbname = "####";     // Your database name
   ?>

3. Run the project locally:

   • Set up a local server using XAMPP, MAMP, or similar tools. Ensure PHP and MySQL are properly installed and running.
   • Place the project files in the htdocs (XAMPP) or equivalent directory for your server setup.
   • Alternatively, since this is a lightweight project, you can use PHP's built-in web server by running:

    php -S localhost:8000

   • Import your database tables via phpMyAdmin or any MySQL management tool.
   • Make sure your database and config.php are correctly set up to allow image uploads and survey data storage.

4. Open index.html in your browser and test the project locally.

5. Ensure the PHP scripts are able to communicate with your MySQL database and that the image upload process functions properly.

Technologies

This project uses the following technologies:

• HTML5 & CSS3
• Bootstrap 5 for UI/UX design
• JavaScript for frontend logic
• TensorFlow.js for client-side model inference
• PHP & MySQL for backend storage
• Chart.js for displaying prediction results
• Microsoft Azure for web hosting

Contributing

Contributions are welcome! If you would like to contribute to the project:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-feature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/new-feature).
5. Create a new Pull Request.

License

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

Contact

For further questions or support:

• Email: jonathangong2005@gmail.com
• GitHub: jogong2718