Fingerprint Integrated Blood Group Testing using Machine Learning Techniques

Detection of blood group is a crucial application in medical diagnostics, particularly in blood transfusions, organ transplantation and prenatal diagnosis. Current methods are invasive and require laboratory facilities, and thus they are time consuming and reliant on trained personnel. This project presents a new, noninvasive method of blood group detection based on fingerprint image processing and machine learning methods. The system utilizes the individual ridge patterns and minutiae points of fingerprints, which are processed through complex image processing algorithms. The Convolutional Neural Networks are used to train the model on a massive fingerprint image dataset with accompanying blood group labels. The use of deep learning architecture allows the system to learn patterns associated with given blood group phenotypes and classify them with high accuracy. This approach offers a speedy, accurate and convenient option to traditional blood typing, which is particularly useful in resource limited settings. Its integration with handheld devices has the potential to facilitate point-of-care diagnostics, decrease reliance on laboratory settings and give immediate results without the requirement of blood samples. The early findings are encouraging, suggesting considerable potential for use in practical clinical applications. This work not only opens doors to new fields of application in biometrics but also meaningfully adds to enhancing healthcare provision through the integration of artificial intelligence and medical science.