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+# Modern Lung Segmentation - Documentation
+
+![Example Tutorial Modern Lung Segmentation](tutor_modern_lung_segmentation.gif)
+
+## Brief Description
+
+Modern Lung Segmentation is an innovative application designed to perform automatic lung segmentation on Chest X-Ray images. This application combines the power of deep learning with a simple and intuitive user interface, allowing users to easily perform fast and accurate lung segmentation.
+
+## Steps to Create the Application
+
+1. Preprocessing Chest X-Ray Dataset
+   - The Chest X-Ray dataset from Kaggle is processed using advanced preprocessing techniques to prepare the data before training the models.
+
+2. Building Deep Learning Models for Segmentation
+   - Powerful and innovative segmentation models are developed in this application. There are four available models: UNet, UNet++, Attention UNet, and R2UNet. These models have proven to provide accurate segmentation results.
+
+3. Model Training
+   - The segmentation models are trained using the preprocessed Chest X-Ray dataset. Training is conducted to optimize the model's parameters and produce accurate and reliable segmentations.
+
+4. Training Evaluation
+   - Evaluation is performed using the Intersection over Union (IoU) metric, which is a commonly used measure to evaluate segmentation quality. Additionally, observations on training time and the number of model parameters are made to provide a comprehensive understanding of the model's performance.
+
+5. Model Saving
+   - After training is complete, the trained segmentation models are saved for future use in the Modern Lung Segmentation application.
+
+6. Creating a User Interface (UI) with Streamlit
+   - The application utilizes Streamlit, a framework for creating interactive UIs, to develop a simple and user-friendly interface.
+   - The features offered by the application are designed to provide a smooth and efficient user experience.
+
+## Key Features of the Application
+
+1. Automatic Segmentation with Four Models
+   - The application provides four pre-trained segmentation models: UNet, UNet++, Attention UNet, and R2UNet.
+   - Users can choose the model that best suits their needs to perform automatic lung segmentation on Chest X-Ray images.
+
+2. Input via Camera and File Upload
+   - The application supports two types of input: users can either capture images directly through their device's camera or upload existing image files.
+
+3. Download Lung Segmentation Results
+   - After performing segmentation on Chest X-Ray images, users can download the generated lung segmentation results provided by the application.
+   - These segmentation results contain valuable information and can be used for further analysis.
+
+4. Model Explanation and Evaluation Menu
+   - The application provides an explanation menu for each available segmentation model. Users can learn the technical details of each model and understand how they work.
+   - Additionally, each model has an evaluation display that provides information about the quality of the segmentation produced by that model.
+
+5. Model Comparison with Informative Charts
+   - The application includes a model comparison menu that showcases segmentation models trained using the Chest X-Ray dataset.
+   - This menu presents informative charts that include IoU evaluation results, computational time, and the number of model parameters.
+   - The charts help users choose the most suitable model for their segmentation predictions.
+
+6. Analysis of Evaluation Results
+   - The evaluation results obtained from the segmentation models can be further analyzed to gain deeper insights into model performance and the quality of the generated segmentations.
+   - This analysis provides a better understanding of the strengths and weaknesses of each model.
+
+## Contributions and Future Development
+We invite you to contribute to the development of the Modern Lung Segmentation application. You can add new features, improve existing segmentation models, or expand the dataset used.
+For more information on contributing and development, please refer to our GitHub repository.
+
+## Contact Us
+If you have any questions, suggestions, or feedback, please feel free to reach out to our team at baurav99@gmail.com.
+
+We appreciate your interest in the Modern Lung Segmentation application and look forward to your involvement in improving healthcare technology.