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@article{192793,
        author = {Vudatha Pavani Sri Lakshmi Savithri and Balaji Rohitha and Bandi Seershika and Kokkirimetti Pavan Naga Kumar and Golthi Soni Harika and Chebrolu Sudeep and Munnangi Manohar},
        title = {Blood Group Detection using Fingerprints},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3037-3042},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192793},
        abstract = {A Deep Learning Approach for Non-Invasive Blood Group Prediction from Fingerprint Images. The determination of blood groups is a critical process in medical emergencies, blood transfusions, and clinical diagnoses. Traditional methods, while accurate, are invasive, time-consuming, and require laboratory setups. This paper proposes a non-invasive, efficient, and cost-effective solution for blood group prediction using fingerprint images. We leverage the power of deep learning to analyze fingerprint patterns and predict the corresponding blood group (A, B, AB, O, and Rh factor). In this study, we have implemented and compared four different Convolutional Neural Network (CNN) architectures: AlexNet, LeNet, VGG16, and ResNet. Our results demonstrate that ResNet provides the highest accuracy, making it the most suitable model for this application. The proposed system has the potential to revolutionize blood group determination, especially in remote areas and emergency situations.},
        keywords = {Blood Group Detection, Fingerprint Recognition, Deep Learning, Streamlit, Convolutional Neural Networks (CNN), ResNet, Image Classification, Biometrics, Health Care, Pattern Recognition, Non-Invasive Diagnostics.},
        month = {February},
        }