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+# Lung Segmentation for RSNA Pneumonia Detection 
+
+## Overview
+This project aims to automatically identify lung opacities in chest x-rays for the RSNA Pneumonia Detection. It is based on the work of Kevin Mader for lung segmentation, as part of the Illuminate AI mentorship program
+
+Medical Image Segmentation involves automatically detecting boundaries within images. In this project, we employ a convolutional neural network with U-Net architecture. The training strategy heavily relies on data augmentation to improve the efficiency of available annotated samples.
+
+Two chest x-ray datasets are used for training:
+- Montgomery County dataset: Includes manually segmented lung masks.
+- Shenzhen Hospital dataset: Manually segmented by Stirenko et al.
+
+The lung segmentation masks from these datasets are dilated to incorporate lung boundary information within the training network, and the images are resized to 512x512 pixels.
+
+## Features
+- Automatic lung opacity identification in chest x-rays.
+- Utilizes U-Net architecture for medical image segmentation.
+- Data augmentation techniques to enhance training efficiency.
+- Incorporation of manually segmented lung masks from two datasets.
+
+## Techniques and Concepts Used
+- Convolutional Neural Networks (CNNs)
+- U-Net Architecture
+- Data Augmentation
+- Image Preprocessing (Resizing, Dilation)
+- Medical Image Segmentation
+
+## How to Run the Notebook
+1. Clone the repository to your local machine:
+
+```bash
+git clone https://github.com/your_username/repository_name.git
+