Automatic Eye Cataract Detection Using YOLOv8

The Automatic Eye Cataract Detection project is a high-accuracy, AI-based solution that benefits from real-time and efficient eye cataract detection using YOLOv8. This system is designed to be user-friendly, scalable, and able to diagnose faster and more accurately in the health service.

• The YOLOv8 model achieved a 99.4% mAP, ensuring reliable detection of cataracts in images and videos.
• The system processes images and videos in real-time, allowing for quick diagnosis in healthcare settings.
• The Gradio-based interface makes it easy for users to upload images or videos and receive instant results, enhancing accessibility for professionals and general users alike.
• The system identifies cataracts at an early stage, enabling timely medical intervention and improving patient outcomes.
• The project is designed to scale, making it adaptable for detecting other eye conditions or handling larger datasets in the future.
• Automation reduces the need for manual inspections, saving time and reducing costs associated with cataract diagnosis.

• The system can analyze both static images and video footage, offering flexibility in how the detection is performed.

• The system's integration with platforms like Google Colab and Roboflow ensures easy model training and deployment, even for users with limited local resources.
• Metrics like mAP, precision, and recall were used to evaluate the model, ensuring consistent and accurate performance during training and validation.
• The system minimizes false positives and false negatives, improving the reliability and accuracy of cataract detection.