README.md

Chest X-ray Pneumonia Classification

Table of Contents

1. About
2. Features
3. Technologies
4. Requirements
5. Starting
6. collaborators
7. Contact
8. License
9. Author

🎯 About

In this project, we develop a convolutional neural network (CNN) model for classifying chest X-rays as either normal or showing signs of pneumonia. The model was trained on a dataset of over 5,863 X-ray images, and achieved an accuracy of 89% on the test set. The architecture of the model consists of multiple convolutional layers, followed by max pooling and dense layers.

dataset we used Chest X-Ray Images (Pneumonia)

full project in details in the documentation

✨ Features

✔️Classify the x-ray imagest to two category ;\

• Pneumonia
• Normal

✔️Used All evaluation metrics

🚀 Technologies

The following tools were used in this project:

• tensorflow==2.12.0
• tqdm
• scikit-learn
• matplotlib
• numpy
• pandas
• pickle
• cv2
• CNN
• Tensorflow, keras
• Jupyter notebook
• deep learning

✅ Requirements

Before starting 🏁, you need to have tensorflow==2.12.0 and all mensioned libraries

🏁 Starting

first create new environment or work in existing one if requirments satsfied

# Clone this project
$ https://github.com/romanyn36/chest_xray_pneumonia_classification_with_cnn.git

use pip to install libraries 
$ pip install tensorflow==2.12.0
$ pip install tqdm
$ pip install scikit-learn          

👥 collaborators

• Ahmed Mohamed Ali             

• Reham Mustafa            

• Sara Reda Moatamed

• Rana Hasan Badrawy          

• Ziad El-Sayed Abdel-Azim    

• Rawan Abdel-Aziz Ahmed    

📧 Contact

📝 License

This project is under license from MIT. For more details.

Made by Romani

 

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