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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 9

Published on: September 2026

AUTOMATED BLOOD GROUP DETECTION USING FINGERPRINT BIOMETRICS AND DEEP LEARNING

Soumya M Venni Usha Sri Badiginchala Hazi Divya Tamanam Manikumar Boddu Thanushya Srilakshmi

Department of Computer Science and Engineering,

Rajiv Gandhi University of Knowledge Technologies (RGUKT), Ongole, Andhra Pradesh, India Z
DOI:https://doi.org/10.55041/ijcope.v2i8.299

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Abstract

Blood group identification is an important requirement in transfusion medicine, emergency care, surgery, and healthcare record management. Conventional ABO and Rh typing is highly established and normally relies on a biological blood sample and serological reactions. This paper presents an artificial-intelligence-based research prototype that investigates whether fingerprint images can be used as a non-invasive input for predicting the eight common ABO/Rh classes: A+, A−, B+, B−, AB+, AB−, O+, and O−. The proposed pipeline accepts a fingerprint image, performs image quality enhancement and normalization, extracts discriminative ridge features using a convolutional neural network (CNN), and assigns the image to a blood-group class. The system is designed as a screening and decision-support prototype rather than a replacement for clinical blood typing. Recent studies have reported promising classification performance on fingerprint datasets, while other clinical and dermatoglyphic investigations have found inconsistent or statistically weak relationships between fingerprints and blood groups. Therefore, this work emphasizes reproducible image processing, supervised learning.

  

KEYWORDS


Fingerprint Analysis, Blood Group Prediction, Machine Learning, Image Processing, Deep Learning, Classification

How to Cite this Paper

M, S., Sri, V. U., Divya, B. H., Manikumar, T. & Srilakshmi, B. T. (2026). Automated Blood Group Detection Using Fingerprint Biometrics and Deep Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i8.299

M, Soumya, et al.. "Automated Blood Group Detection Using Fingerprint Biometrics and Deep Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.299.

M, Soumya,Venni Sri,Badiginchala Divya,Tamanam Manikumar, and Boddu Srilakshmi. "Automated Blood Group Detection Using Fingerprint Biometrics and Deep Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.299.

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References


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Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Sep 02 2026
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