iDentify is a web-based application designed to facilitate the detection and classification of dental anomalies from panoramic X-rays. The tool consists of two main components: a React Vite-based front-end application and a backend system incorporating a trained YOLOv9-c model wrapped in a FastAPI container.

The frontend is built using React Vite, a modern web development framework that allows for efficient and scalable development. The application offers several functionalities, including:

• Welcome page
• Sign-up and login (using Firebase Authentication)
• Dashboard for image upload, result viewing, zoom feature, and PDF export with detailed text descriptions of detected dental anomalies
• Feedback page for model performance feedback

The backend is a FastAPI application responsible for:

• Receiving and processing image data
• Running the detection algorithm
• Returning the results along with confidence scores for each classification of dental anomalies (Caries, Deep caries, Periapical lesion, and Impacted tooth)

You have two options to run the backend:

1. Using Docker: Pull the Docker image from Docker Hub and run it:

   docker pull kanwalmehreen/identifyapp:latest
   docker run -p 8000:8000 kanwalmehreen/identifyapp:latest

2. Running Locally:

   1. Clone the repository:

      git clone https://github.com/kawiiii/iDentify.git
      cd iDentify/api

   2. Create a virtual environment:

      python -m venv venv

   3. Activate the virtual environment:
      • On Windows:

        venv\Scripts\activate

      • On macOS and Linux:

        source venv/bin/activate

   4. Install dependencies:

      pip install -r requirements.txt

   5. Download the model from Google Drive and place it in the api/models directory.
   6. Run the backend:

      uvicorn main:app --reload

You can access the backend at http://0.0.0.0:8000. Press CTRL+C to quit.

1. Clone the repository if you haven't already.
2. Navigate to the frontend directory:

   cd iDentify/frontend

3. Copy the .env.example file to create a new .env file:

   cp .env.example .env

   Fill in the Firebase keys and any other required environment variables in the newly created .env file.
4. Install the dependencies:

   npm install

5. Install react-modal:

   npm install react-modal

6. Run the frontend development server:

   npm run dev

Once the development server is running, you can access the frontend application at http://localhost:5174/ .

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

• Kanwal Mehreen - Technical Editor & ML Researcher (Email: [email protected])
• Hassan Aqeel Khan - Senior Lecturer, Applied AI & Robotics (Email: [email protected])