Abstract :

Blood Group Detection Plays A Critical Role In Medical Emergencies, Transfusion Medicine, And Forensic Science. Traditional Methods Often Rely On Laboratory-based Tests, Which Can Be Time Consuming And Require Skilled Personnel. This Project Proposes An Automated, Efficient, And Non- Invasive Method For Identifying Blood Groups Using Deep Convolutional Neural Networks (CNNs). Leveraging The Power Of Deep Learning, The System Analyses Microscopic Images Of Blood Samples To Accurately Classify Blood Groups Into ABO And Rh Categories. The Proposed Model Is Trained On A Diverse Dataset To Ensure Robust Performance Across Various Sample Conditions. The Architecture Optimizes Feature Extraction And Classification, Achieving High Accuracy While Minimizing Computational Overhead. This Approach Has The Potential To Revolutionize Point-of-care Diagnostics By Providing Rapid And Reliable Blood Group Identification, Thus Saving Lives And Improving Healthcare Accessibility. Index-Terms:- Blood Group Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Point-of-Care Diagnostics, ABO And Rh Blood Groups, Medical Imaging, Automated Healthcare Systems.

Published:

29-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

363-370

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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