AI-Based Blood Group Classification and Prediction Using Image Processing

Abstract

Accurate identification of blood groups is essential in medical diagnostics, transfusion management, and emergency healthcare. Conventional blood typing methods are typically manual, time-consuming, and require skilled laboratory personnel, which can lead to delays and potential human errors. This paper presents an automated system titled “AI-Based Blood Group Classification and Prediction Using Image Processing,” which leverages artificial intelligence and computer vision techniques to improve the efficiency and accuracy of blood group detection.The proposed system utilizes image processing methods to analyze microscopic images of blood samples and identify agglutination patterns associated with different blood groups. Features are extracted using techniques such as segmentation, edge detection, and pattern recognition, and are then classified using machine learning and deep learning models such as Convolutional Neural Networks (CNN). The system is capable of accurately classifying blood groups (A, B, AB, and O) and predicting Rh factors with high precision. Experimental results demonstrate that the approach significantly reduces processing time, minimizes human intervention, and improves diagnostic reliability, making it a valuable tool for automated and scalable blood typing in healthcare systems.