Pneumonia Detection Using Deep Learning

This project leverages deep learning techniques to automatically detect pneumonia from chest X-ray images. It utilizes a Convolutional Neural Network (CNN) to classify X-ray scans as either Normal or Pneumonia.

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🛠️ Getting Started

1. Clone the Repository

git clone https://github.com/Alex0-7/Pneumonia-Detection-using-Deep-Learning.git

2. Download the Dataset

Download the chest X-ray dataset from Kaggle and extract it into your project directory:

Dataset Link: Chest X-Ray Pneumonia Dataset

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📦 Installation

Install the required dependencies using the requirements.txt file:

pip install -r requirements.txt

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🚀 Running the Model

Step 1: Update File Paths in Pneumonia.py

Before running the training script, update the following lines in Pneumonia.py with your local dataset paths:

• Line 10
• Line 11
• Line 16
• Line 32
• Line 36

Then, run the script:

python Pneumonia.py

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Step 2: Test the Model Using test.py

To evaluate the model on validation data:

1. Open the test.py file.
2. Update line 11 with the correct path to the PNEUMONIA folder inside the val directory.
3. Run the test script:

python test.py

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✅ Output

The model will output predictions for the input X-ray images, indicating whether the image is Normal or shows signs of Pneumonia.

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📌 Notes

• Ensure the dataset folder structure aligns with the script expectations.
• Training time may vary based on your hardware (e.g., CPU vs GPU).

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🤝 Acknowledgments

• Dataset provided by Paul Mooney on Kaggle
• Inspired by real-world applications in medical image analysis and diagnostics.

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Feel free to contribute, suggest improvements, or report issues!