Pneumonia Prediction Project

This project aims to predict pneumonia from chest X-ray images using a deep learning model. The application consists of a React front-end for user interaction and a Flask back-end to handle predictions.

Model Structure

The convolutional neural network (CNN) for predict pneumonia uses the Keras library. It includes convolutional layers with batch normalization and max-pooling, followed by dense layers. The model uses regularization, dropout, and is compiled with the Adam optimizer. Training includes early stopping, learning rate reduction, and TensorBoard logging. The accuracy is 83.166 %.

Project Structure

• react/: React application for the user interface.
• server/: Flask application for serving the machine learning model and handling predictions.

Getting Started

Frontend

1. Navigate to the react/ directory.
2. Install dependencies:

npm install

3. Navigate to the v2d/ directory.
4. Start the react app

npm start

5. The React app will run on http://localhost:3000.

Backend

1. Navigate to the backend/ directory.

2. Create a virtual environment:

python -m venv venv

3. Activate the virtual environment:

.\venv\Scripts\activate

4. Install dependencies:

pip install -r requirements.txt

5. Run the Flask application:

python app.py

The Flask app will run on http://localhost:5000.

Usage

1. Open your web browser and go to http://localhost:3000.
2. Upload a chest X-ray image through the interface.
3. The deep learning model will predict whether pneumonia is present or not.
4. View the prediction result on the web page.

Contributing

If you'd like to contribute to this project, please follow the standard GitHub flow:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/new-feature
3. Commit your changes: git commit -m 'Add new feature'
4. Push to the branch: git push origin feature/new-feature
5. Create a pull request.