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:

     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! 🌟👩‍⚕️🔍