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Automated Blood Group Detection Using Computer Vision and Transfer Learning on Mobile-Optimized Deep Neural Networks

Domaine:

healthcare

Type de record:

model
Créateur:
Mit
Éditeur:
Zenodo
Hôte:avatar
This research presents an AI-assisted automated blood group detection framework using computer vision and MobileNetV2-based transfer learning for point-of-care biomedical diagnostics. The proposed system combines COCO annotation-driven region extraction, OpenCV preprocessing, and lightweight deep learning classification to identify agglutination patterns across Anti-A, Anti-B, and Anti-D reagent zones from full blood-card images. The dataset contains 7,836 annotated blood-card images:• Training Images: 6,456• Validation Images: 997• Test Images: 383 The model achieves:• ≈94–95% zone-level accuracy• ≈91–93% end-to-end ABO accuracy• Macro F1-score: 0.947 The system is optimized for lightweight mobile deployment using TensorFlow Lite and is designed as a research-stage assistive AI diagnostic framework for low-resource healthcare environments. This work does not claim clinical certification or replacement of laboratory blood typing procedures.

Visit

doi.orgzenodo.org

Tasks

image classificationcomputer vision

Tags

Computer VisionDeep learningBiomedical AIBlood Group DetectionMobileNetV2Medical Image ProcessingHealthcare AI

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcodeCopyright (C) 2026 The Authors.http://rightsstatements.org/vocab/InC/1.0/

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