Fingerprint-Based Blood Group Prediction Using Deep Learning: A Study of Dermatoglyphic Patterns

This paper reformulates a project report describing a web-based fingerprint image classification system for exploratory blood-group prediction. Its workflow combines image preprocessing, CNN inference, a Flask interface, and prediction-history storage. The implementation provides a software foundation for experimentation, but the available report does not establish clinical validity or quantified predictive performance. Future work should prioritize a scientifically justified study design, larger and more diverse datasets, participant-independent evaluation, comparison of model architectures, explainability methods, secure biometric-data handling, and external validation. Any result should be clearly labelled experimental until independently validated against established serological testing.

[1] Y. Aamir, R. Masood, N. Irshad, R. Malik, N. Farid, and M. A. Shahab, “Relationship Between Pattern of Fingerprints and Blood Groups,” 2021. Full publication details should be verified. [2] G. Perween, A. K. Prasad, S. Hussain, and A. Abhishek, “Analysis of Thumb Print Patterns in Relation with Blood Group Among Medical Students: A Cross-Sectional Study,” 2022. Full publication details should be verified. [3] M. Prasad and Amrutha, “Blood Group Detection Through Fingerprint Images Using Image Processing,” 2023. Full publication details should be verified. [4] R. Senthilkumar, M. Harish, R. M., and A. Navinkumar, “Innovative Blood Group Prediction Using Fingerprint Patterns,” 2024. Full publication details should be verified. [5] G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, 2017. [6] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Proc. MICCAI, 2015. [7] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [8] C. Szegedy et al., “Going Deeper with Convolutions,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2015. [9] TensorFlow, “TensorFlow Documentation.” Available: https://www.tensorflow.org/ (accessed Oct. 2026).