lokeshv790/streamlit_pneumonia_detection_deeplearnig

The Pneumonia Detection App is a web application designed to assist in the diagnosis of pneumonia using chest X-ray images. This project utilizes deep learning techniques implemented with TensorFlow and Keras for image classification, and is deployed using Streamlit for a user-friendly interface.

Pneumonia Detection from Chest X-Ray Images using Deep Learning CNN 🩺

Overview

The Pneumonia Detection App is a web application built to assist in the diagnosis of pneumonia using chest X-ray images. It utilizes deep learning techniques implemented with TensorFlow and Keras for image classification, and is deployed using Streamlit for a user-friendly interface.

Features 🚀

• Upload Image: Users can upload a chest X-ray image to the app.
• Prediction: The app predicts whether the uploaded image indicates pneumonia.
• Comparison: Provides visual comparison with sample normal and pneumonia chest X-ray images.
• User-Friendly Interface: Built with Streamlit to ensure easy navigation and interaction.

Try it Out 🔍

You can try the app here.

Technologies Used 🛠️

• TensorFlow: Deep learning framework for model training and prediction.
• Keras: High-level neural networks API (running on TensorFlow) used for building and training the deep learning models.
• Streamlit: Open-source app framework used to build interactive web applications for machine learning and data science.
• Python: Programming language used for development.
• matplotlib, Pillow (PIL): Python libraries used for image visualization and processing.

Installation 📦

1. Clone the Repository:

   • Clone the repository to your local machine.

2. Install Dependencies:

   • Install Python dependencies listed in `requirements.txt`.

Usage 🖥️

1. Run the Streamlit App:

   • Start the Streamlit application locally.

2. Upload an Image:

   • Use the app interface to upload a chest X-ray image.

3. View Results:

   • The app will display the uploaded image and provide a prediction for pneumonia.
   • Compare the uploaded image with sample normal and pneumonia images provided by the app.

Screenshots 📸

Mobile Demo

Desktop Demo

Contributing 🤝

We welcome contributions to improve the app. Here's how you can contribute:

• Fork the repository
• Create your feature branch (`git checkout -b feature/YourFeature`)
• Commit your changes (`git commit -am 'Add some feature'`)
• Push to the branch (`git push origin feature/YourFeature`)
• Create a new Pull Request

License 📜

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

Acknowledgements 🙏

• Mention any libraries or resources that you used or were inspired by during development.

Contact 📧

For questions or feedback, feel free to contact Lokesh Vazirani.