Beyond Blood Tests: Using Deep Learning to Identify Blood Groups through Fingerprint Analysis

Particularly in an emergency where fast identification is needed, medical diagnostics depend on exact and non-invasive blood group detection. Conventional blood type techniques involving serological analysis ask for direct blood samples and might be time-consuming. Aside from creating new opportunities for quick, contactless blood group identification, this work advances biometric-based medical diagnostics. Because of their tenacity and individuality, fingerprints possessing their distinct ridge patterns have always been utilized in biometric identification. By offering a rapid, reasonably priced, and safe replacement for traditional blood group detection, this method opens the path for advancements in biometric-driven medical diagnostics. In this research, we propose a harmless deep learning based technique using fingerprint photos for blood group classification that aims to extract and assess fingerprint patterns utilizing biometric correlations with blood group characteristics.