Abstract: Detection of blood groups is a vital process in transfusion medicine, as well as in emergency care and individualized treatment strategies. Traditional methods of blood typing include serological testing through the use of blood samples and laboratory-based antigen-antibody reactions, which are time-consuming, invasive, and resource intensive. To address these issues, the following research suggests a deep learning approach to detection of blood groups using two different techniques: fingerprint patterns and images of blood smears. Through the use of Convolutional Neural Networks (CNNs) to perform feature extraction and classification, this system hopes to offer a quick, painless, and precise complement to mainstream blood typing procedures. Fingerprint typing relies on the theory that dermatoglyphic patterns are associated with genetic components and may be related to blood groups.

High-resolution fingerprint images have high-resolution imaging processed through CNNs to identify the low-level features that can distinguish between them. Additionally, deep learning models interpret images of blood smears to determine cell morphology patterns that identify particular blood types. The system under consideration is developed with Python as backend processing, Flask for web-based communication, and HTML, CSS, and JavaScript for interface purposes. The process minimizes reliance on physical blood sample collection, thus making it extremely applicable to remote and resource-scarce regions. It increases access, reduces errors in blood typing, and accelerates emergency medical response.

However, difficulties including availability of datasets, bias in algorithms, and generalizability of models need to be resolved for clinical deployment. Future efforts will involve enlarging training datasets, improving deep learning architectures, and incorporating real-time mobile apps for general adoption. The suggested system represents a milestone in AI-based medical diagnostics, providing a practical and scalable method for detecting blood groups.

Keywords: Blood Group Prediction, Machine Learning in Healthcare, Neural Networks, Image Processing, Deep Learning-Based Biometrics, AI in Medical Diagnostics, Pattern Recognition in Medicine, Fingerprint Recognition, Medical Image Analysis, Healthcare AI Applications, Feature Extraction Techniques, Biometric Authentication, Automated Blood Typing, CNN-Based Classification, Digital Pathology, Non-Invasive Medical Testing, Blood Type Identification Using AI, Healthcare Informatics, Medical Data Processing

| DOI: 10.17148/IARJSET.2025.12226