Published Paper Details:

Blood tests that show your group type support both medical testing and blood transfusions while responding to emergencies. The performance of standard blood typing assessments needs specialized laboratory equipment alongside technical specialists and takes significant processing duration. The proposed research presents a non-invasive approach to forecast blood groups directly from fingerprints which cuts down testing expenses and speeds up identification processes. The study employs Inception V3 on fingerprint samples from Kaggle to detect blood group information. The study evaluates depth learning enhancements caused by RMSprop SGD alongside Adam through their performance in blood group predictions along with reduced training durations. The procedural method demonstrates successful blood type identification from fingerprint pictures according to experimental validation results. Our method provides a blood type testing transformation that advances low-resourced medical facilities. The research team aims to develop models that perform effectively with various population groups through system implementation for healthcare facilities.