Blood Group Detection and Management Using Advanced Deep Learning and Fingerprint Imaging Methods

Abstract: Fingerprints, unique and permanent, have been one of the most reliable biometric markers. The presence and arrangement of these ridges is established via fetal development and never change during your life, serving as a means to uniquely identify and verify identity. Utilizing this uniqueness, the project offers a comprehensive solution of advanced fingerprint image processing and a deep-learning approach to blood group detection. The system uses Convolutional Neural Networks (CNNs) to examine complex ridge patterns present in fingerprint images for accurate blood group classification. This approach integrates the trustworthiness of biometric fingernail pattern personal identification with the speed of machine learning, providing a non-invasive and accurate alternative in healthcare. The project also features a comprehensive blood management system that efficiently communicates between hospitals and blood donors, especially during emergencies. This system allows for more than accurate blood group detection by helping better utilize resources, in turn reducing response times in critical situations. By providing a scalable and adaptable framework, such technology can not only promote better blood management but also spur advancements in healthcare as well as in biometric research