Pneumonia Detection Model using X-ray Images Description This repository contains a deep learning model trained to distinguish between normal human lungs and those affected by pneumonia. It is tailored to use X-ray images sourced from radiography labs, but is robust enough to analyze images from the internet or custom uploads from users.
Features High Accuracy: The model has been trained with extensive datasets, ensuring high precision and recall rates. Versatility: Accepts X-ray images from various sources including direct uploads from users. User-Friendly Interface: Easily upload an X-ray image and receive an instantaneous diagnosis.
Ensure you have Python (>=3.6) installed. Required Python libraries: TensorFlow (or PyTorch), Flask, PIL, etc. (Check requirements.txt for a complete list.)
#Installation Clone the repository:
Copy code git clone Navigate to the cloned directory and install the required packages:
Copy code cd pneumonia-detection-model pip install -r requirements.txt
Start the web application: bash Copy code python app.py Open a web browser and navigate to http://127.0.0.1:5000/.
Use the upload button to submit an X-ray image of the lungs.
The system will process the image and display whether the X-ray indicates pneumonia or not.
Data The model was trained using X-ray images from radiography labs. While this ensures a high degree of accuracy for images from similar sources, the model has also been tested and optimized for general X-ray images found online or from personal users.
#Contributing Contributions are welcome! Please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.
#License This project is licensed under the MIT License. See LICENSE.md for details.
Acknowledgments Radiography labs that provided the datasets for training. Open-source community for invaluable tools and libraries.