/Pneumonia_Detection_Team_Project

This project utilizes deep learning to detect pneumonia from chest X-ray images, offering both model training and real-time inference through Jupyter Notebooks and a Flask web application. With a focus on flexibility and user-friendliness, it empowers users to fine-tune model parameters and seamlessly deploy the trained model for accurate pneumonia

Primary LanguagePython

Pneumonia Detection using CNN

Overview

This project aims to detect pneumonia in chest X-ray images using Convolutional Neural Networks (CNN). It includes a Jupyter notebook for model training and evaluation and a Flask web application for real-time inference on user-uploaded images.

Requirements

  • Python 3.x
  • TensorFlow
  • pandas
  • NumPy
  • Matplotlib
  • scikit-learn
  • Flask
  • Streamlit

Installation

  1. Clone the repository:

    git clone https://github.com/Alikhizar142/pneumonia-detection.git

  2. Install the required dependencies:

    pip install -r requirements.txt

Usage

Jupyter Notebook (Model Training and Evaluation)

  1. Load the dataset: Ensure you have downloaded and placed the dataset appropriately. You can modify the paths in the code if necessary.

  2. Execute the notebook: Run the provided Jupyter notebook pneumonia_detection.ipynb to go through the data preprocessing, model building, training, and evaluation steps.

  3. Adjust parameters: You can tweak the model architecture, data augmentation techniques, and hyperparameters to improve performance.

  4. Evaluate the model: After training, evaluate the model's performance on the test set and analyze the results.

Flask Web Application

  1. Run the Flask application:

    python flask_app.py

  2. Visit http://localhost:5000 in your web browser to use the Flask web application.

Streamlit Web Application

  1. Run the Streamlit application:

    streamlit run app.py

  2. Open your web browser and visit the URL displayed in the terminal to access the Streamlit web application.

Web Application

The Flask web application allows users to upload chest X-ray images and receive real-time predictions on whether pneumonia is detected.

  • /: Home page with an upload form to submit images for prediction.
  • /about: About page providing information about the project.

Model

The trained model (my_model3.h5) is loaded into the Flask application for inference. It uses a pre-trained VGG16 architecture fine-tuned for pneumonia detection.

License

This project is licensed under the MIT License.

Acknowledgements

  • The dataset used in this project is sourced from Kaggle.
  • We acknowledge the developers of TensorFlow, scikit-learn, Flask, and other open-source libraries used in this project.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.