This Jupyter Notebook demonstrates the process of lung segmentation from chest X-ray images using deep learning techniques. The notebook utilizes a pre-trained convolutional neural network (CNN) model to segment the lung regions, enabling the extraction of relevant features for further analysis or medical diagnosis.
To run the notebook successfully, ensure you have the following dependencies installed:
- Python 3.x
- Jupyter Notebook
- TensorFlow
- Keras
- OpenCV
- NumPy
- Matplotlib
It is recommended to use a virtual environment to manage the dependencies and avoid conflicts with existing installations.
The notebook assumes that you have a dataset of chest X-ray images available for lung segmentation. Please ensure that the dataset is organized in an appropriate directory structure and each image is labeled accordingly.
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Clone the repository to your local machine:
git clone https://github.com/your-username/lung-segmentation.git](https://github.com/Mdwij/lung-segmentation.git)
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Navigate to the project directory:
cd lung-segmentation -
Start Jupyter Notebook:
jupyter notebook
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Open the
Lung_segmentation_from_Chest_X_Ray.ipynbnotebook in your browser. -
Follow the step-by-step instructions provided in the notebook to load the dataset, preprocess the images, and perform lung segmentation using the pre-trained CNN model.
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Execute the notebook cells sequentially to see the results and visualize the segmented lung regions.
If you have a different dataset structure or want to experiment with different models or parameters, you can customize the notebook accordingly. Feel free to modify the code and adapt it to your specific requirements.
The notebook generates segmented lung masks for each input chest X-ray image. You can visualize and analyze the results to gain insights into lung regions and potentially integrate them into other medical image analysis pipelines.
This notebook builds upon the contributions of various researchers and developers in the field of medical image analysis and deep learning. Please refer to the notebook's references section for the relevant papers and resources.
The code in this repository is provided under the MIT License. However, please note that the pre-trained models or any other external resources used within the notebook may have their own licenses and usage restrictions. Make sure to review and comply with the respective licenses when utilizing external resources.
For any questions or inquiries, please feel free to reach out to the repository owner or the project contributors listed in the notebook.