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https://github.com/mohammad95labbaf/lung-segmentation

The preparation for the Lung X-Ray Mask Segmentation project included the use of augmentation methods like flipping to improve the dataset, along with measures to ensure data uniformity and quality. The model architecture was explored with two types of ResNets: the traditional CNN layers and Depthwise Separable.
https://github.com/mohammad95labbaf/lung-segmentation

chest-xray-images chest-xrays cnn cnn-keras convolutional-neural-networks deep-learning deep-neural-networks deeplearning depthwise-separable-convolutions depthwiseseparableconvolution lung-disease lung-segmentation segment segmentation x-ray-images

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The preparation for the Lung X-Ray Mask Segmentation project included the use of augmentation methods like flipping to improve the dataset, along with measures to ensure data uniformity and quality. The model architecture was explored with two types of ResNets: the traditional CNN layers and Depthwise Separable.

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README 

        
# Lung-Segmentation



This repository contains code and resources for segmenting abnormal regions in chest X-ray images using deep learning techniques. The goal is to accurately identify pneumonia, tumors, or other lung-related abnormalities.



## Dataset

- The dataset used in this project is sourced from Kaggle and is named `nikhilpandey360/chest-xray-masks-and-labels`.

- It includes original chest X-ray images along with corresponding lung masks.

- Ensure you have downloaded the dataset before proceeding.



## Data Preparation

1. **Data Augmentation**:

    - Apply data augmentation techniques (e.g., flipping, rotation) to increase dataset diversity.

    - Augmented data helps improve model generalization.



2. **Preprocessing**:

    - Crop or resize images to focus on the lung area.

    - Normalize pixel values to a consistent range (e.g., [0, 1]).



## Model Architecture

- We experimented with two residual ResNets:

    1. **Conventional ResNet**:

        - Utilizes standard CNN layers.

        - Captures hierarchical features through residual connections.

    2. **Depthwise Separable ResNet**:

        - Employs depthwise separable convolutions for efficiency.

        - Reduces the number of parameters while maintaining performance.



## Performance Evaluation

- We assessed model performance using the **Dice coefficient** (F1 score).

- This metric quantifies the overlap between predicted and true positive regions.



## Visualization

- Created segmentation figures to visualize model predictions.

- Compare predicted masks with actual abnormalities in X-rays.



## Next Steps

- Fine-tune hyperparameters, explore additional architectures, or incorporate other evaluation metrics.

- Continue refining the model based on performance and domain-specific requirements.



## Instructions for Kaggle API

1. **Download Kaggle API**:

    - Install the Kaggle API by running `pip install kaggle`.



2. **Kaggle API Token**:

    - Go to your Kaggle account settings and generate an API token.

    - Save the token as `kaggle.json` in the root directory of this repository.



3. **Download Dataset**:

    - Use the Kaggle API to download the dataset:

        ```

        kaggle datasets download -d nikhilpandey360/chest-xray-masks-and-labels

        ```



4. **Upload Kaggle API Token to Colab/Notebook**:

    - If using Colab or Jupyter Notebook, upload the `kaggle.json` token to your environment.

    - Use the following code snippet:

        ```python

        from google.colab import files

        files.upload()

        ```



5. **Unzip Dataset**:

    - Unzip the downloaded dataset:

        ```

        unzip chest-xray-masks-and-labels.zip

        ```



Feel free to explore, contribute, and enhance this project! 🌟👩‍⚕️🔍