https://github.com/djdhairya/pneumonia-detection
https://youtu.be/1SQIrxhMuUs?si=lF2cg8eTnETf-5Qx
https://github.com/djdhairya/pneumonia-detection
cnn cv deep-learning flask gunicorn keras matplotlib opencv pandas pillow scikit-learn seaborn tensorflow vgg19
Last synced: 7 months ago
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https://youtu.be/1SQIrxhMuUs?si=lF2cg8eTnETf-5Qx
- Host: GitHub
- URL: https://github.com/djdhairya/pneumonia-detection
- Owner: djdhairya
- License: mit
- Created: 2025-03-24T18:35:42.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-24T18:40:29.000Z (7 months ago)
- Last Synced: 2025-03-24T19:38:02.512Z (7 months ago)
- Topics: cnn, cv, deep-learning, flask, gunicorn, keras, matplotlib, opencv, pandas, pillow, scikit-learn, seaborn, tensorflow, vgg19
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
- Size: 2.85 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Pneumonia Detection
This project focuses on developing a deep learning model to detect pneumonia from chest X-ray images using the dataset provided by Paul Timothy Mooney on Kaggle. The model leverages the VGG19 architecture and is further fine-tuned for improved performance.
## Dataset
The dataset used for this project is available on Kaggle:
[Chest X-Ray Images (Pneumonia) Dataset](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)
It comprises chest X-ray images categorized into 'PNEUMONIA' and 'NORMAL' classes, divided into training, validation, and test sets.
## Installation
To set up the project environment:
1. **Clone the Repository**:
```bash
git clone https://github.com/djdhairya/Pneumonia-Detection.git
cd pneumonia-detection
```
2. **Install Required Packages**:
```bash
pip install -r requirements.txt
```
## Libraries Used 📚
- **TensorFlow / Keras** – For building and training the CNN model.
- **NumPy** – For numerical computations.
- **Pandas** – For data handling and preprocessing.
- **Matplotlib** – For visualizing training history and results.
- **Seaborn** – For creating advanced visualizations.
- **scikit-learn** – For data splitting, evaluation metrics, and confusion matrix.
- **OpenCV** – For image processing (optional based on usage).
- **Flask** – For deploying the model as a web application.
- **Gunicorn** – WSGI server (optional for deployment).
- **Werkzeug** – Used in backend routing (via Flask).
- **Pillow (PIL)** – For image manipulation in Python.
---
## Usage
1. **Train the Model**:
- Run the Jupyter notebook files provided to preprocess the dataset and train the model.
- Model weights will be saved in the `model_weights/` directory.
2. **Run the Web Application**:
- Start the Flask server:
```bash
python app.py
```
- Open the browser and navigate to `http://127.0.0.1:5000/` to upload chest X-ray images and get predictions.
## Contributing
Contributions are welcome. Please fork the repository and submit a pull request with your improvements.
## License
This project is licensed under the MIT License.
