Radiology-Website-For-Processing-X-Ray-Images-Using-Deep-Learning This project is made at ENSI as a final-year project, it is a Radiology platform for radiologists, doctors, and patients that a provides a better healthcare experience. You can watch the video Demo here.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Configuration
• Development
• Deployment
• Contributing

Getting Started

To get the Radiology Platform Django Website up and running on your local machine, follow these steps:

Prerequisites

You will need the following software installed on your system:

• Python 3.x
• Django
• virtualenv (optional but recommended)

Installation

1. Clone this repository to your local machine:

   git clone (https://github.com/hadilbenmoussa/Radiology-Website-For-Processing-X-Ray-Images-Using-Deep-Learning.git)

2. Navigate to the project directory:

   cd radiology_platfrom

3. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate
   

4. Install the required packages:

    Copy code
    pip install -r requirements.txt

5. Apply the database migrations:

   python manage.py migrate

6. Create a superuser account for the admin panel:

    python manage.py createsuperuser

7. Start the development server:

   python manage.py runserver

Your Radiology Platform Django Website should now be running locally at http://localhost:8000/.

Usage

Access the admin panel at http://localhost:8000/admin/ to manage appointments, radiology reports, blog posts, and user accounts. Visit the blog homepage at http://localhost:8000/ to view and interact with the website. Configuration You can customize the website's behavior and appearance by modifying the settings in settings.py. Additionally, you can set environment variables for sensitive information such as secret keys.

Development

If you'd like to contribute to this project, please follow these guidelines:

Fork the repository on GitHub. Create a new branch with a descriptive name. Make your changes and test thoroughly. Submit a pull request, explaining the purpose of your changes.

Deployment

For production deployment, it's recommended to use a production-ready web server like Gunicorn, and a database server like PostgreSQL. Detailed deployment instructions are beyond the scope of this README, but you can find resources and guides online.

Contributing

We welcome contributions! Feel free to open issues or pull requests to help improve this project.