Medical Waste Detection and Classification Through YOLO Algorithms

Muhammad H.Bin Moktar; Sami Salama Hussen Hajjaj; Hassan Mohamed

doi:10.1007/978-3-031-70687-5_3

Medical Waste Detection and Classification Through YOLO Algorithms

Muhammad H.Bin Moktar^*
, Sami Salama Hussen Hajjaj
, Hassan Mohamed

^*Corresponding author for this work

School of Robotics

Universiti Tenaga Nasional

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

General waste is commonly managed to reduce pollution. Similarly, medical waste can be classified and managed to not only reduce pollution but also mitigate health risks and accidental injuries. Medical waste includes a variety of materials such as those contaminated with body fluids, sharps waste, and chemical waste. This study evaluates modern Artificial Intelligence methods for classifying medical waste such as facemasks, gloves, and syringes. Various classification models, including CNN, ResNet50, YOLO v3, and YOLO v4, were used and compared. YOLO v4 achieves a higher mAP (89.21%), surpassing YOLO v3 and other YOLO models used in waste classification studies. YOLO v4 was then tested in object detection and successfully identified masks, gloves, and syringes. Further performance evaluations are necessary to enhance the detection of medical waste and other objects in various applications.

Original language	English
Title of host publication	Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
Editors	Anwar P. P. Abdul Majeed, Eng Hwa Yap, Pengcheng Liu, Xiaowei Huang, Anh Nguyen, Wei Chen, Ue-Hwan Kim
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	22-33
Number of pages	12
ISBN (Print)	9783031706868
DOIs	https://doi.org/10.1007/978-3-031-70687-5_3
Publication status	Published - 2024
Event	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 - Taicang, China Duration: 6 Dec 2023 → 8 Dec 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1133 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Country/Territory	China
City	Taicang
Period	6/12/23 → 8/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence
Deep Learning
Machine Learning

Access to Document

10.1007/978-3-031-70687-5_3

Cite this

Moktar, M. H. B., Hajjaj, S. S. H., & Mohamed, H. (2024). Medical Waste Detection and Classification Through YOLO Algorithms. In A. P. P. Abdul Majeed, E. H. Yap, P. Liu, X. Huang, A. Nguyen, W. Chen, & U.-H. Kim (Eds.), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications (pp. 22-33). (Lecture Notes in Networks and Systems; Vol. 1133 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70687-5_3

Moktar, Muhammad H.Bin ; Hajjaj, Sami Salama Hussen ; Mohamed, Hassan. / Medical Waste Detection and Classification Through YOLO Algorithms. Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. editor / Anwar P. P. Abdul Majeed ; Eng Hwa Yap ; Pengcheng Liu ; Xiaowei Huang ; Anh Nguyen ; Wei Chen ; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. pp. 22-33 (Lecture Notes in Networks and Systems).

@inproceedings{b8266b5f143c4c68aff5b46ea015275a,

title = "Medical Waste Detection and Classification Through YOLO Algorithms",

abstract = "General waste is commonly managed to reduce pollution. Similarly, medical waste can be classified and managed to not only reduce pollution but also mitigate health risks and accidental injuries. Medical waste includes a variety of materials such as those contaminated with body fluids, sharps waste, and chemical waste. This study evaluates modern Artificial Intelligence methods for classifying medical waste such as facemasks, gloves, and syringes. Various classification models, including CNN, ResNet50, YOLO v3, and YOLO v4, were used and compared. YOLO v4 achieves a higher mAP (89.21\%), surpassing YOLO v3 and other YOLO models used in waste classification studies. YOLO v4 was then tested in object detection and successfully identified masks, gloves, and syringes. Further performance evaluations are necessary to enhance the detection of medical waste and other objects in various applications.",

keywords = "Artificial Intelligence, Deep Learning, Machine Learning",

author = "Moktar, \{Muhammad H.Bin\} and Hajjaj, \{Sami Salama Hussen\} and Hassan Mohamed",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023 ; Conference date: 06-12-2023 Through 08-12-2023",

year = "2024",

doi = "10.1007/978-3-031-70687-5\_3",

language = "English",

isbn = "9783031706868",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "22--33",

editor = "\{Abdul Majeed\}, \{Anwar P. P.\} and Yap, \{Eng Hwa\} and Pengcheng Liu and Xiaowei Huang and Anh Nguyen and Wei Chen and Ue-Hwan Kim",

booktitle = "Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications",

}

Moktar, MHB, Hajjaj, SSH & Mohamed, H 2024, Medical Waste Detection and Classification Through YOLO Algorithms. in APP Abdul Majeed, EH Yap, P Liu, X Huang, A Nguyen, W Chen & U-H Kim (eds), Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Lecture Notes in Networks and Systems, vol. 1133 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 22-33, 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023, Taicang, China, 6/12/23. https://doi.org/10.1007/978-3-031-70687-5_3

Medical Waste Detection and Classification Through YOLO Algorithms. / Moktar, Muhammad H.Bin; Hajjaj, Sami Salama Hussen; Mohamed, Hassan.
Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. ed. / Anwar P. P. Abdul Majeed; Eng Hwa Yap; Pengcheng Liu; Xiaowei Huang; Anh Nguyen; Wei Chen; Ue-Hwan Kim. Springer Science and Business Media Deutschland GmbH, 2024. p. 22-33 (Lecture Notes in Networks and Systems; Vol. 1133 LNNS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Medical Waste Detection and Classification Through YOLO Algorithms

AU - Moktar, Muhammad H.Bin

AU - Hajjaj, Sami Salama Hussen

AU - Mohamed, Hassan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - General waste is commonly managed to reduce pollution. Similarly, medical waste can be classified and managed to not only reduce pollution but also mitigate health risks and accidental injuries. Medical waste includes a variety of materials such as those contaminated with body fluids, sharps waste, and chemical waste. This study evaluates modern Artificial Intelligence methods for classifying medical waste such as facemasks, gloves, and syringes. Various classification models, including CNN, ResNet50, YOLO v3, and YOLO v4, were used and compared. YOLO v4 achieves a higher mAP (89.21%), surpassing YOLO v3 and other YOLO models used in waste classification studies. YOLO v4 was then tested in object detection and successfully identified masks, gloves, and syringes. Further performance evaluations are necessary to enhance the detection of medical waste and other objects in various applications.

AB - General waste is commonly managed to reduce pollution. Similarly, medical waste can be classified and managed to not only reduce pollution but also mitigate health risks and accidental injuries. Medical waste includes a variety of materials such as those contaminated with body fluids, sharps waste, and chemical waste. This study evaluates modern Artificial Intelligence methods for classifying medical waste such as facemasks, gloves, and syringes. Various classification models, including CNN, ResNet50, YOLO v3, and YOLO v4, were used and compared. YOLO v4 achieves a higher mAP (89.21%), surpassing YOLO v3 and other YOLO models used in waste classification studies. YOLO v4 was then tested in object detection and successfully identified masks, gloves, and syringes. Further performance evaluations are necessary to enhance the detection of medical waste and other objects in various applications.

KW - Artificial Intelligence

KW - Deep Learning

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/85211356838

U2 - 10.1007/978-3-031-70687-5_3

DO - 10.1007/978-3-031-70687-5_3

M3 - Conference Proceeding

AN - SCOPUS:85211356838

SN - 9783031706868

T3 - Lecture Notes in Networks and Systems

SP - 22

EP - 33

BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications

A2 - Abdul Majeed, Anwar P. P.

A2 - Yap, Eng Hwa

A2 - Liu, Pengcheng

A2 - Huang, Xiaowei

A2 - Nguyen, Anh

A2 - Chen, Wei

A2 - Kim, Ue-Hwan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

Y2 - 6 December 2023 through 8 December 2023

ER -

Moktar MHB, Hajjaj SSH, Mohamed H. Medical Waste Detection and Classification Through YOLO Algorithms. In Abdul Majeed APP, Yap EH, Liu P, Huang X, Nguyen A, Chen W, Kim UH, editors, Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications. Springer Science and Business Media Deutschland GmbH. 2024. p. 22-33. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-70687-5_3

Medical Waste Detection and Classification Through YOLO Algorithms

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