Deep Learning Based Blood Group Detection Using Fingerprint

Blood group identification is an essential process in medical diagnostics, particularly for blood transfusion, emergency care, and organ transplantation. Conventional blood typing methods rely on laboratory testing, which can be time consuming and requires trained medical personnel. Recent advancements in artificial intelligence and biometric analysis have introduced alternative techniques for predicting blood groups using fingerprint patterns. This paper proposes a deep learning–based approach for blood group detection using fingerprint images. The system utilizes convolutional neural networks (CNN) to extract fingerprint features and classify them into ABO and Rh blood groups. The proposed model performs image preprocessing, feature extraction, and classification to achieve accurate prediction. The study also reviews existing image processing and artificial intelligence techniques for blood group prediction and compares their effectiveness. The results demonstrate that deep learning models can provide a fast and noninvasive method for preliminary blood group identification.