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Multi-Scale Attention Networks with Feature Refinement for Medical Item Classification in Intelligent Healthcare

Waqar Riaz1,2, Asif Ullah1,2, Jiancheng Charles Ji1

  • 1Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

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This study introduces a hybrid deep learning framework for accurate medical item recognition in healthcare inventory. The AI system achieves high precision in detecting and classifying diverse medical supplies, improving inventory management.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Intelligent healthcare systems require accurate medical imaging for inventory management.
  • Efficient recognition and classification of medical items are crucial for inventory integrity and timely access to resources.

Purpose of the Study:

  • To present a hybrid deep learning framework, EfficientDet-BiFormer-ResNet, for robust medical item detection and classification in healthcare inventory.
  • To enhance the accuracy and efficiency of AI-driven medical inventory solutions.

Main Methods:

  • Developed a hybrid deep learning framework integrating EfficientDet (BiFPN), BiFormer (attention), and ResNet-18 (triplet loss, OHNM).
  • Trained and validated the model on a custom dataset of over 5000 medical inventory images under varied conditions.
Keywords:
AI-enabled healthcareEfficientDet-BiFormer integrationYOLOclinical inventory classificationimage sensing in healthcaremask RCNN

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Last Updated: Apr 30, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K
  • Evaluated performance using mean average precision (mAP) and top-1 classification accuracy.

    • Main Results:

    • Achieved a mean average precision (mAP@0.5:0.95) of 83.2% and a top-1 classification accuracy of 94.7%.
    • Outperformed conventional models like YOLO, SSD, and Mask R-CNN.
    • Demonstrated superior performance in recognizing visually similar, occluded, and small-scale medical items.

    Conclusions:

    • The EfficientDet-BiFormer-ResNet framework provides an effective AI-enabled vision system for real-time medical inventory management.
    • This advancement supports clinically relevant and accurate detection of medical items in healthcare settings.
    • The proposed system enhances inventory integrity and ensures timely access to critical medical supplies.