Blood Group Detection with Fingerprint Using Deep Learning

Blood group detection is a crucial aspect of medical diagnostics, ensuring compatibility in transfusions, organ transplants, and prenatal care. Traditional methods of blood group determination involve serological techniques, which, while accurate, require invasive procedures and laboratory infrastructure. This paper explores an innovative approach to blood group detection through fingerprint image processing. Leveraging the unique ridge patterns and minutiae points in fingerprints, this non-invasive method aims to provide a rapid, reliable, and accessible means of determining blood groups. Our proposed system employs advanced image processing algorithms and machine learning techniques to analyse fingerprint images, correlating specific patterns with blood group phenotypes. The integration of this method into portable and cost-effective devices can revolutionise point-of-care diagnostics, particularly in resource-limited settings. Preliminary results demonstrate promising accuracy levels, highlighting the potential for further development and implementation in clinical practice. This research opens new avenues in biometric applications and contributes significantly to enhancing healthcare delivery through innovative technological solutions. In recent years, blood group detection has become vital in various medical and forensic applications. Traditional blood typing methods are often time-consuming and require skilled personnel, limiting their accessibility and efficiency. This study explores an innovative approach utilising fingerprint image processing and Convolutional Neural Network (CNNs) for accurate and rapid blood group detection. The proposed method leverages the unique ridge patterns in fingerprints, which have been found to correlate with specific blood group types. By employing a CNN architecture, the system is trained on a substantial dataset of fingerprint images labelled with corresponding blood groups. The model demonstrates high accuracy in identifying blood groups, showcasing the potential of CNNs in biometrics-based blood typing. This approach promises a non-invasive, quick, and reliable alternative to conventional blood group detection methods, enhancing the efficacy of medical diagnostics and transfusion services. The results indicate a significant step forward in integrating biometric data with medical diagnostics, paving the way for further advancements in the field.