Pneumonia Detection from Chest X-rays

This project leverages deep learning to classify chest X-ray images and detect the presence of pneumonia.

Table of Contents

• Overview
• Installation and Setup
• Usage
• Results
• Acknowledgements

Overview

Pneumonia is an inflammatory condition of the lung affecting primarily the small air sacs known as alveoli. Early detection of pneumonia through X-rays can significantly improve treatment efficacy. This project implements a Convolutional Neural Network (CNN) model using TensorFlow to classify chest X-ray images as 'Normal' or 'Pneumonia'.

Installation and Setup

1. Clone the repository:

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

2. Navigate to the directory:

cd pneumonia-detection

3. Set up a virtual environment (optional but recommended):

python3 -m venv env
source env/bin/activate

4. Install required packages:

pip install -r requirements.txt

(Note: This assumes that you have a requirements.txt file with necessary packages.)

Usage

1. Data Preparation:

   • Place normal chest X-ray images in /content/normal
   • Place pneumonia chest X-ray images in /content/pneumonia

2. Training:

   • Run the Jupyter Notebook or Python script to initiate the training process. The model will be saved as pneumonia_classifier.h5.

3. Evaluation:

   • Use the provided evaluation script or Jupyter cells to evaluate the model's performance on the test set.

Results

The model achieved an accuracy of 91.91% on the test set. A confusion matrix provides further details on the model's performance:

(Insert confusion matrix image here)

Acknowledgements

• Dataset source: Chest X-Ray Images (Pneumonia) from Kaggle
• Thanks to TensorFlow for the deep learning framework.

MIT License

Copyright (c) [2023] [Tamagha Nag]

Permission is hereby granted, free of charge, to any person obtaining a copy of this project and associated documentation files, to deal in the project without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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