Enhanced Blood Group Prediction with Fingerprint Images using Deep Learning

Blood group prediction is a crucial process in medical diagnostics and emergency care, traditionally carried out through serological tests that require blood samples and specialized laboratory equipment. These conventional methods, while accurate, are invasive, time-consuming, and may be impractical in situations where resources are limited or unavailable in urgent situations. This research explores a novel approach of using fingerprint images for blood group detection, applying deep learning, specifically Convolutional Neural Networks (CNN) with good architectures. By analyzing the unique patterns in fingerprints, these models may offer a non-invasive, faster, and more efficient alternative to traditional methods. This study examines the feasibility of this approach, providing insights into how deep learning could potentially be utilized for blood group prediction.