Welcome to the image_segmentation project! This application helps you segment COVID-19 affected lungs using advanced techniques. You can track experiments and compare models with ease. Let’s get started with the download process!
To begin, visit the page below to download the application:
Before you download and install the software, ensure your system meets the following requirements:
- Operating System: Windows 10, macOS, or Linux
- RAM: At least 4 GB
- Disk Space: Minimum of 500 MB available
- Processor: Dual-core processor or better
- COVID-19 Lung Segmentation: Utilizes U-Net and MobileNet-U-Net architectures for accurate segmentation.
- Experiment Tracking: Uses MLflow to keep records of your experiments.
- Model Comparison: Easily compare results of different models in one interface.
- User-Friendly Interface: Designed for all users, regardless of technical skills.
- Download the Application: Click the link to download from Releases.
- Locate the File: Once downloaded, find the file in your downloads folder.
- Install the Application:
- For Windows, double-click the
.exefile and follow the instructions. - For macOS, drag the application to your Applications folder and open it from there.
- For Linux, extract the downloaded file and run the executable from your terminal.
- For Windows, double-click the
- Open the Application: Launch the app from your Applications or Programs menu.
- Load Your Images: Click on the “Upload” button to select images of lung scans.
- Run Segmentation: After uploading, click on “Start Segmentation” to process the images.
- View Results: Once processing is complete, view the segmented images displayed on your screen.
- Save Results: Use the “Save” button to download the segmented images for your records.
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Issue: The application won’t open.
- Solution: Ensure your operating system meets the minimum requirements. Check for software updates on your OS.
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Issue: Uploading images fails.
- Solution: Verify the file format. Supported formats include PNG and JPEG.
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Issue: Segmentation results are not accurate.
- Solution: Make sure the images are clear and properly cropped.
To learn more about how this application works and the technology behind it, check the following resources:
- U-Net Architecture: Understand how U-Net improves image segmentation.
- MobileNet Benefits: Learn why MobileNet is a popular choice for deep learning applications.
- MLflow Documentation: Familiarize yourself with tracking experiments using MLflow.
For further assistance, take advantage of our community:
- GitHub Issues: Report problems or ask questions on the GitHub issues page.
- Community Forums: Join discussions and share insights with fellow users.
Thank you for using image_segmentation! Your contribution to the fight against COVID-19 is greatly appreciated.