AI-Driven Classification & Prediction Of Blood Groups Through Image Processing

Abstract

The classification and prediction of blood groups are crucial for various medical and emergency applications, including blood transfusion, organ transplantation, and forensic investigations. Traditional methods of blood group determination rely on serological testing, which can be time-consuming and prone to human error. This research proposes an AI-driven approach for classifying and predicting blood groups based on microscopic images of blood samples using advanced image processing and machine learning techniques. The process involves preprocessing blood sample images to extract key features, followed by training deep learning models, specifically Convolutional Neural Networks (CNNs), to identify patterns in the red blood cells and classify them into the four primary blood groups: A, B, AB, and O. The model is trained using a large dataset of labeled blood sample images and evaluated for its accuracy and robustness. This automated approach offers a faster, more accurate, and scalable solution for blood group classification, with potential applications in medical diagnostics, emergency healthcare, and bioinformatics.