This project aims to build a deep learning model for the detection of pneumonia in chest X-ray images. The model is implemented using TensorFlow and Keras, leveraging convolutional neural networks (CNNs) for image classification. The dataset used for training and evaluation includes chest X-ray images from patients with and without pneumonia.
The dataset used in this project is the Chest X-Ray Images (Pneumonia) available on Kaggle. Please download and organize the dataset before running the project. The dataset includes subdirectories for training, validation, and testing.
data/: Contains the dataset for training, validation, and testing.notebooks/: Jupyter notebooks for data exploration, model development, and evaluation.src/: Source code for the deep learning model and any preprocessing scripts.docs/: Documentation files, including project overview, model architecture, and usage instructions.
- Clone the repository:
git clone https://github.com/[your-username]/pneumonia-detection.git - Navigate to the project directory:
cd pneumonia-detection - Set up the Python environment:
pip install -r requirements.txt - Download and organize the dataset in the
data/directory. - Run the Jupyter notebooks in the
notebooks/directory for data exploration and model development.
To train the pneumonia detection model, use the provided Python scripts in the src/ directory. Adjust hyperparameters, model architecture, and paths to the dataset as needed.
python src/train.py --data_dir data/train --epochs 10 --batch_size 32
Evaluation
Evaluate the trained model on the test dataset and visualize predictions using the provided Jupyter notebooks.
bash
Copy code
python src/evaluate.py --model_path models/pneumonia_model.h5 --test_data_dir data/test
Results
The model achieves an accuracy of [insert accuracy here]% on the test set. For detailed results, refer to the results/ directory.
Contributing
Feel free to contribute by opening issues, proposing new features, or submitting pull requests. Your contributions are welcome!