IWaste: Video-Based Medical Waste Detection and Classification

Junbo Chen, Jeffrey Mao, Cassandra Thiel, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Waste auditing is important for effectively reducing the medical waste generated by resource-intensive operating rooms. To replace the current time-intensive and dangerous manual waste auditing method, we propose a system named iWASTE to detect and classify medical waste based on videos recorded by a camera-equipped waste container. In this pilot study, we collected a video dataset of 4 waste items (gloves, hairnet, mask, and shoecover) and designed a motion detection based preprocessing method to extract and trim useful frames. We propose a novel architecture named R3D+C2D to classify waste videos by combining features learnt by 2D convolutional and 3D convolutional neural networks. The proposed method obtained a promising result (79.99% accuracy) on our challenging dataset.Clinical Relevance - iWaste enables consistent and effective real-time monitoring of solid waste generation in operating rooms, which can be used to enforce medical waste sorting policies and to identify waste reduction strategies.

Original languageEnglish (US)
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5794-5797
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period7/20/207/24/20

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Chen, J., Mao, J., Thiel, C., & Wang, Y. (2020). IWaste: Video-Based Medical Waste Detection and Classification. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (pp. 5794-5797). [9175645] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC44109.2020.9175645