Blood Group Typing using CNN

Blood group typing is a critical procedure in transfusion medicine, organ transplantation, and pregnancy management. Ensuring patient safety requires accurate and rapid blood typing, as incompatible transfusions can lead to severe complications, including hemolytic reactions. Traditional serological methods are labor-intensive, time-consuming, and prone to human error due to reliance on manual interpretation. This study presents an automated approach to blood group classification using Convolutional Neural Networks (CNNs), a deep learning algorithm renowned for its proficiency in image analysis. Leveraging a labeled dataset of blood sample images, the proposed model achieved an overall classification accuracy of 87.50%. Additionally, precision and recall values for each blood group classification were notably high, underscoring the model’s reliability and effectiveness. By identifying specific patterns and features in blood sample images, the CNN-based system enhances diagnostic efficiency, minimizes human intervention, and reduces errors, particularly in resource-limited clinical settings. This work demonstrates the feasibility of integrating artificial intelligence into blood typing workflows, contributing to modernized transfusion practices and improved patient outcomes.