A Vision-Based Approach for Blood Group Detection Using Image Processing

Accurate is essential in blood group detection medical situations such as trauma response, organ transplantation, and blood transfusion services. Historically, blood type analysis was based on manual serological methods employing skilled technicians and visual examination, which, while successful, are time-consuming and prone to human error. These limitations pose significant risks, especially in emergency or resource-constrained environments. To tackle this problem, this project puts forward a digitized blood group detection system leveraging image processing techniques. The system utilizes high-resolution images of blood samples mixed with standard reagents (Anti-A, Anti-B, and AntiD) to detect agglutination patterns. Using Python libraries such as OpenCV, the software processes these images to identify the ABO blood group and Rh factor with minimal human intervention. A user-friendly graphical interface enables operation by nonspecialists, making the tool ideal for deployment in ambulances, rural clinics, and field blood camps. Experimental results demonstrate the system’s capability to detect blood groups with high precision and significantly reduced processing time compared to traditional methods. Its offline functionality and hardware-independent design further enhance accessibility and usability. The framework is scalable and adaptable, with potential future enhancements including integration of AI models for complex blood analysis and mobile platform deployment.