Pneumonia Classification using Deep Learning

Welcome to the Pneumonia Classification repository! This project aims to provide an accurate and efficient deep learning model for identifying various stages of pneumonia in X-ray images of the lungs. By leveraging the power of Convolutional Neural Networks (CNNs) and the TensorFlow library, we have developed a robust solution that can predict pneumonia stages with high accuracy.

Table of Contents

• Introduction
• Model Architecture
• API Usage
• Installation
• Usage
• Contributing
• License

Introduction

Pneumonia is a severe respiratory infection that affects millions of people worldwide. Timely and accurate diagnosis plays a vital role in ensuring proper treatment and care for patients. This project addresses the challenge of automating the pneumonia classification process, allowing medical professionals to make informed decisions quickly.

Model Architecture

Our deep learning model utilizes a Convolutional Neural Network (CNN) architecture, a powerful technique in image recognition tasks. The model has been trained on a large dataset of labeled X-ray images, encompassing various pneumonia stages: Fusion beat (F), Unknown beat (Q), Normal beat (N), Supraventricular ectopic beat (S), and Ventricular ectopic beat (V).

API Usage

We have created a convenient API using the Flask framework to facilitate easy integration of our model into web and mobile applications. The API enables users to upload an X-ray image and receive a prediction of the corresponding pneumonia stage. This allows for seamless integration into existing healthcare systems or custom applications.

Installation

To get started with the Pneumonia Classification project, follow these steps:

1. Clone the repository: git clone https://github.com/your-username/pneumonia-classification.git
2. Install the required dependencies: pip install -r requirements.txt
3. Download the pre-trained weights for the CNN model (link: weights.zip) and place them in the models directory.
4. Set up the Flask environment variables (e.g., FLASK_APP=app.py) or configure your preferred deployment method.

Usage

Once you have completed the installation, you can run the project and start making predictions. Here's a simple guide to getting started:

1. Start the Flask API server: flask run
2. Make a POST request to the /predict endpoint with the X-ray image data. You can use various methods like cURL, Postman, or integrate it into your application using your preferred programming language.

import requests

image_path = '/path/to/xray_image.jpg'
url = 'http://localhost:5000/predict'

# Read the image file
with open(image_path, 'rb') as image_file:
    image_data = image_file.read()

# Send the image data to the API
response = requests.post(url, files={'image': image_data})

# Retrieve the prediction
prediction = response.json()['prediction']
print(f"The predicted pneumonia stage is: {prediction}")

Contributing

We welcome contributions from the community to improve and enhance this pneumonia classification project. If you have any ideas, bug fixes, or feature suggestions, please feel free to submit a pull request. Together, we can make a positive impact in the field of medical diagnostics.

License

This project is licensed under the MIT License.

---

We hope you find the Food Delivery Application useful and look forward to your contributions. If you have any questions or need assistance, please reach out to us. Happy coding!

Made with ❤️ by Gimnath Perera