Pneumonia Detection Using Deep Learning

Overview

Leverage deep learning techniques for the automated detection of pneumonia from medical imaging data. This repository provides end-to-end Jupyter Notebooks and Python utilities for preprocessing, modeling, evaluation, and prediction.

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Features

• End-to-End Jupyter Notebooks: All stages from data preprocessing to model evaluation and prediction.
• Image Classification: Utilize convolutional neural networks (CNNs) for reliable pneumonia detection.
• Data Augmentation: Methods to enhance dataset size and diversity.
• Visualization: Tools for visualizing images, model performance, and predictions.
• Reproducibility: Results are easily reproducible and customizable for new datasets.

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Repository Structure

• *.ipynb – Core experiments and workflows (Jupyter Notebooks, main project focus)
• .py (auxiliary) – Helper scripts for data handling and preprocessing
• /data – Directory placeholder for medical images and labels
• /models – Saved weights and model architectures

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Requirements

• Primary Language: Jupyter Notebook (Python 3.x kernel)
• Libraries:
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • scikit-learn
  • tensorflow / keras
  • pillow
  • opencv-python

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Setup

1. Clone the repository

   git clone https://github.com/willow788/Pneumonia-detection-using-Deep-Learning.git
   cd Pneumonia-detection-using-Deep-Learning

2. Install required packages

   pip install -r requirements.txt

3. Add your dataset

   • Place medical images and labels in the /data directory, as described in the notebooks.

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Usage

• Open relevant Jupyter Notebooks using your preferred environment (JupyterLab, Colab, etc.).
• Follow the step-by-step instructions in the notebooks to:
  • Prepare and visualize data
  • Train deep learning models
  • Evaluate model performance
  • Use trained models to predict pneumonia on new images

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Results

• Achieves effective detection accuracy as demonstrated in the evaluation notebook.
• Example results and confusion matrices are visualized in the provided notebooks.

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Contributing

• Pull requests and suggestions are welcome.
• Please open issues for questions, feature requests, or bug reports.

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License

Distributed under the MIT License. See LICENSE for details.

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