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Pneumonia Detection from Chest X-Ray

This project is a web-based application that leverages a deep learning model to predict whether a given chest X-ray indicates pneumonia. The application is implemented using Streamlit, PyTorch, and other essential libraries to provide an easy-to-use interface for medical image analysis.

Table of Contents

  1. Project Overview
  2. Features
  3. Architecture
  4. Installation and Usage
  5. Model Training
  6. Results
  7. Future Work
  8. Contributing
  9. License

Project Overview

This project aims to assist healthcare professionals and individuals by providing an AI-driven tool for pneumonia detection using chest X-ray images. The web app accepts an uploaded image, preprocesses it, and outputs a prediction along with a confidence score. It serves as an informational tool and emphasizes that it should not replace professional medical diagnosis.

Features

  • Upload and Analyze X-Ray Images: Users can upload chest X-ray images in JPEG format for analysis.
  • Deep Learning Model: Powered by a convolutional neural network (CNN) based on an extended TinyVGG architecture.
  • Real-Time Predictions: Displays the prediction result (Normal or Pneumonia) with a confidence score.
  • User-Friendly Interface: Built with Streamlit for intuitive interaction.

Architecture

Model Architecture:

The project employs a custom CNN architecture with the following key components:

  • Multiple convolutional blocks with ReLU activation and MaxPooling.
  • Fully connected layer for classification.
  • Grayscale input preprocessing.

Tools and Libraries:

  • Streamlit: For building the interactive web application.
  • PyTorch: For model loading, inference, and tensor operations.
  • Pillow (PIL): For image processing.
  • Torchvision: For image transformations.

Installation and Usage

Prerequisites:

  • Python 3.8 or higher.
  • PyTorch (compatible with your hardware and OS).
  • Streamlit and other required Python libraries.