Smart Biometrics: Using Fingerprint Data for Blood Group Classification

Identification of blood groups is a vital part of medical diagnostics and is necessary for organ transplants, safe blood transfusions, and prenatal care. Because traditional blood typing methods depend on chemical reagents and blood samples, they are intrusive, time-consuming, and resource-intensive. In this study, fingerprint analysis is used to classify blood groups in a non-invasive manner. The unique fingerprint characteristics linked to blood types are extracted by utilising sophisticated image processing techniques. Convolutional Neural Networks (CNN) and ResNet architectures are used in our deep learning-based categorisation, feature extraction, and picture enhancement process. A thorough dataset is used to evaluate the suggested model, which shows encouraging accuracy and effectiveness in blood group prediction. With its quick, affordable, and easily accessible solution, this method has the potential to completely transform conventional blood typing and is especially advantageous in settings with limited resources or distant locations. With bigger datasets and better deep learning models, accuracy could be further increased in the future, making fingerprint-based blood type detection a competitive alternative to current techniques.