Deep Learning-Based Blood Group Detection System Using Patient Haemoglobin Data

Blood group identification is a critical component in various medical scenarios, especially during emergencies such as surgeries and transfusions. Traditional serological methods, while accurate, are time-consuming, invasive, and reliant on specialized laboratory facilities. This project introduces an innovative approach that leverages deep learning techniques to detect blood groups using haemoglobin levels — a readily available physiological parameter. By training neural network models on datasets comprising haemoglobin values and their corresponding blood groups, the proposed system aims to automate and expedite the blood group detection process. This method not only reduces the dependency on laboratory procedures but also presents a non-invasive, cost-effective, and scalable solution for rapid diagnosis. The system architecture incorporates data pre processing , feature engineering, deep learning model training, evaluation, and deployment, resulting in a practical tool that can be integrated with existing medical infrastructure for real-time blood group prediction.