By Hema Kalyan Murapaka

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Special Thanks to GitHub Sponsors

Medical imaging has transformed healthcare by providing detailed insights into various diseases, particularly in the chest area. However, the current reliance on manual interpretation of imaging data by radiologists leads to delays, errors, and inefficiencies in diagnosing chest diseases. With a growing demand for healthcare services and a shortage of radiologists in some areas, there's an urgent need for automated systems to accurately detect and classify chest diseases from imaging data. These systems would not only improve diagnostic accuracy and efficiency but also alleviate strain on healthcare resources, enhancing patient care and outcomes.

• Tensorflow==2.12.0
• Pandas
• GDown
• DVC
• MLFlow==2.2.2
• Flask

This will help you understand how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Follow these steps to install and set up the project directly from the GitHub repository:

1. Clone the Repository

   • Open your terminal or command prompt.
   • Navigate to the directory where you want to install the project.
   • Run the following command to clone the GitHub repository:

     git clone https://github.com/KalyanM45/End-to-End-Chest-Disease-Classification.git
     

2. Create a Virtual Environment (Optional but recommended)

   • It's a good practice to create a virtual environment to manage project dependencies. Run the following command:

     conda create -p <Environment_Name> python==<python version> -y
     

3. Activate the Virtual Environment (Optional)

   • Activate the virtual environment based on your operating system:

     conda activate <Environment_Name>/
     

4. Install Dependencies

   • Navigate to the project directory:

     cd [project_directory]
     

   • Run the following command to install project dependencies:

     pip install -r requirements.txt
     

5. Run the Project

   • Start the project by running the appropriate command.

     python app.py
     

6. Access the Project

   • Open a web browser or the appropriate client to access the project.

If you prefer to use Docker, you can install and run the project using a Docker container from DockerHub:

1. Pull the Docker Image

   • Open your terminal or command prompt.
   • Run the following command to pull the Docker image from DockerHub:

     docker pull kalyan45/Chest-detection-app
     

     This command downloads the Docker image from the DockerHub.

2. Run the Docker Container

   • Start the Docker container by running the following command. Adjust the port mapping as needed:

     docker run -p 5000:5000 kalyan45/Chest-detection-app
     

     This command launches the project within a Docker container.

3. Access the Project

   • Open a web browser or the appropriate client to access the project.
     

To use this project, you need an API key from Google Gemini Large Language Model. Follow these steps to obtain and set up your API key:

1. Get API Key:

   • Visit the Provided Link Click Here.
   • Follow the instructions to create an account and obtain your API key.

2. Set Up API Key:

   • Create a file named .env in the project root.
   • Add your API key to the .env file:

     API_KEY=your_api_key_here

   Note: Keep your API key confidential. Do not share it publicly or expose it in your code.
   

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

• Report bugs: If you encounter any bugs, please let us know. Open up an issue and let us know the problem.

• Contribute code: If you are a developer and want to contribute, follow the instructions below to get started!

1. Fork the Project
2. Create your Feature Branch
3. Commit your Changes
4. Push to the Branch
5. Open a Pull Request

• Suggestions: If you don't want to code but have some awesome ideas, open up an issue explaining some updates or improvements you would like to see!

This project is licensed under the Open Source Initiative (OSI) approved GNU General Public License v3.0 License - see the LICENSE.txt file for details.

Hema Kalyan Murapaka - [email protected]

We'd like to extend our gratitude to all individuals and organizations who have played a role in the development and success of this project. Your support, whether through contributions, inspiration, or encouragement, has been invaluable. Thank you for being a part of our journey.