Biometric-Based Blood Group Detection Using Deep Learning

In emergency medical situations, rapidly determining a person's blood type is crucial, especially when prompt and safe blood transfusion is necessary. Conventional methods like serological testing, although dependable, often involve drawing blood and using laboratory equipment, which can delay results and limit accessibility in emergencies or remote areas lacking medical facilities.To address these issues, this research introduces a novel and non-inavasive approach that uses fingerprint patterns and deep learning to determine blood groups. A Convolutional Neural Network (CNN), known for its ability to recognize patterns in images, is used to examine fingerprint ridges and accurately classify both ABO and Rh blood groups.The system was developed and tested using a well-organized and accurately labeled dataset, allowing the model to learn effectively. The results revealed that the method performs with high accuracy, showing its potential as a practical alternative to traditional blood testing techniques. This approach is not only fast and easy to use but also adaptable for use in fieldwork, emergency healthcare, and mobile diagnostics where traditional testing is not feasible.Beyond emergency use, the fingerprint-based system could also be applied in health ID cards, biometric authentication systems, and automated hospital processes, providing healthcare professionals with quick access to critical patient information. This work contributes meaningfully to the development of AIdriven healthcare solutions by offering a unique integration of biometrics and medical diagnostics for enhanced patient care