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Updated: Oct 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A deep learning approach for medical waste classification.

Haiying Zhou1, Xiangyu Yu2, Ahmad Alhaskawi1

  • 1Department of Orthopedics, The First Affiliated Hospital, College of Medicine, Zhejiang University, #79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, People's Republic of China.

Scientific Reports
|February 10, 2022
PubMed
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This summary is machine-generated.

This study introduces a deep learning method for accurate medical waste classification, achieving 97.2% accuracy in identifying 8 waste types. This AI approach addresses the urgent need for efficient medical waste management.

Area of Science:

  • Computer Science
  • Environmental Science
  • Healthcare Technology

Background:

  • Growing healthcare demand leads to increased medical waste generation, posing environmental challenges.
  • Accurate classification of medical waste is crucial for proper disposal and environmental protection.
  • Existing methods face limitations, necessitating advanced solutions for effective medical waste management.

Purpose of the Study:

  • To propose and evaluate a deep learning-based approach for the automatic identification and classification of medical waste.
  • To address the limitations of deep learning in medical waste classification by employing transfer learning.
  • To develop a practical and highly accurate AI solution for the urgent problem of medical waste categorization.

Main Methods:

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  • A deep learning model utilizing ResNeXt architecture was implemented for image classification.
  • Transfer learning techniques were applied to enhance classification performance.
  • The model was trained and validated on a dataset of 3480 medical waste images, covering 8 distinct categories.

    • Main Results:

    • The proposed deep learning method achieved a high accuracy of 97.2% in identifying 8 types of medical waste.
    • The average F1-score across five-fold cross-validation was also 97.2%, indicating robust performance.
    • The study demonstrated the effectiveness of AI in automatic medical waste detection and classification with high precision.

    Conclusions:

    • Deep learning, particularly with ResNeXt and transfer learning, offers a powerful and accurate solution for medical waste classification.
    • The developed AI method can significantly improve the efficiency and accuracy of medical waste management.
    • This technology holds potential for widespread application in China to facilitate environmental protection efforts.