Automated Blood Group Detection System Using Image Processing for Non- Invasive Antigen Feature Extraction and Classification

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods.”