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@article{196715,
        author = {Nageswari Lakku and B.Shanawaz Baig and midasala sannuthi and mullamuri aparna},
        title = {Fingerprint-Based Blood Group Prediction Using Deep Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3309-3314},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196715},
        abstract = {Blood group identification is an essential medical requirement before transfusion and emergency treatment. Conventional laboratory testing requires reagents, equipment, and trained personnel. This research proposes a biometric-based blood group prediction system using fingerprint images and deep learning. The system employs a convolutional neural network based on MobileNetV2 transfer learning to classify fingerprints into eight blood group categories. Fingerprint images are preprocessed, augmented, and trained using a labeled dataset. The trained model is integrated into a web-based application for real-time prediction. Experimental results show that the model can classify fingerprint patterns with moderate accuracy, demonstrating the feasibility of non-invasive blood group prediction for academic research purposes.},
        keywords = {Biometric identification, Blood group prediction, Deep learning, Transfer learning, MobileNetV2, Fingerprint classification},
        month = {April},
        }