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Real-Time Object Detection System for Hospital Assets Using YOLOv8

Hospital administration is essential in the provision of high-quality social services to patients. Hospitals must have efficient asset management to offer quality medical care. On the other hand, many hospitals face problems such as data entry errors. Based on this problem, the author hopes to solve...

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Main Authors: Andalusia, Friska, Suakanto, Sinung, Hamami, Faqih, Mat Raffei, Anis Farihan, Nuryatno, Edi
Format: Conference or Workshop Item
Language:English
Published: IEEE 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/41502/1/Real-Time_Object_Detection_System_for_Hospital_Assets_Using_YOLOv8.pdf
http://umpir.ump.edu.my/id/eprint/41502/
https://doi.org/10.1109/ICE3IS62977.2024.10775948
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spelling my.ump.umpir.415022025-01-15T01:07:55Z http://umpir.ump.edu.my/id/eprint/41502/ Real-Time Object Detection System for Hospital Assets Using YOLOv8 Andalusia, Friska Suakanto, Sinung Hamami, Faqih Mat Raffei, Anis Farihan Nuryatno, Edi QA75 Electronic computers. Computer science Hospital administration is essential in the provision of high-quality social services to patients. Hospitals must have efficient asset management to offer quality medical care. On the other hand, many hospitals face problems such as data entry errors. Based on this problem, the author hopes to solve it by implementing real-time object detection and recording data distribution using the YOLO (You Only Look Once) algorithm. This data distribution will then be applied to the current system. Performance tests were carried out in this research using the YOLO architecture, especially on YOLOv8. one of the improvements of popular deep learning algorithms. This research used 7680 images (augmentation) which were divided into 3 parts. 6720 training data (88%), 640 validation data (8%), and 320 (4%) test data. 7680 data were added from 16 tested medical device categories with 200 images per category. This research has an average accuracy of 90%, an average precision of 94%, and an average recall value of 92.2%. These results show that YOLOv8 performs well in detecting medical devices. To improve accuracy, it is recommended to test larger and more diverse datasets. This research helps the healthcare industry better monitor and manage real-time assets. IEEE 2024-12-11 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41502/1/Real-Time_Object_Detection_System_for_Hospital_Assets_Using_YOLOv8.pdf Andalusia, Friska and Suakanto, Sinung and Hamami, Faqih and Mat Raffei, Anis Farihan and Nuryatno, Edi (2024) Real-Time Object Detection System for Hospital Assets Using YOLOv8. In: 2024 4th International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS). 2024 4th International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) , 07-08 August 2024 , Yogyakarta, Indonesia. pp. 403-408.. ISBN 979-8-3503-7836-8 (Published) https://doi.org/10.1109/ICE3IS62977.2024.10775948
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Andalusia, Friska
Suakanto, Sinung
Hamami, Faqih
Mat Raffei, Anis Farihan
Nuryatno, Edi
Real-Time Object Detection System for Hospital Assets Using YOLOv8
description Hospital administration is essential in the provision of high-quality social services to patients. Hospitals must have efficient asset management to offer quality medical care. On the other hand, many hospitals face problems such as data entry errors. Based on this problem, the author hopes to solve it by implementing real-time object detection and recording data distribution using the YOLO (You Only Look Once) algorithm. This data distribution will then be applied to the current system. Performance tests were carried out in this research using the YOLO architecture, especially on YOLOv8. one of the improvements of popular deep learning algorithms. This research used 7680 images (augmentation) which were divided into 3 parts. 6720 training data (88%), 640 validation data (8%), and 320 (4%) test data. 7680 data were added from 16 tested medical device categories with 200 images per category. This research has an average accuracy of 90%, an average precision of 94%, and an average recall value of 92.2%. These results show that YOLOv8 performs well in detecting medical devices. To improve accuracy, it is recommended to test larger and more diverse datasets. This research helps the healthcare industry better monitor and manage real-time assets.
format Conference or Workshop Item
author Andalusia, Friska
Suakanto, Sinung
Hamami, Faqih
Mat Raffei, Anis Farihan
Nuryatno, Edi
author_facet Andalusia, Friska
Suakanto, Sinung
Hamami, Faqih
Mat Raffei, Anis Farihan
Nuryatno, Edi
author_sort Andalusia, Friska
title Real-Time Object Detection System for Hospital Assets Using YOLOv8
title_short Real-Time Object Detection System for Hospital Assets Using YOLOv8
title_full Real-Time Object Detection System for Hospital Assets Using YOLOv8
title_fullStr Real-Time Object Detection System for Hospital Assets Using YOLOv8
title_full_unstemmed Real-Time Object Detection System for Hospital Assets Using YOLOv8
title_sort real-time object detection system for hospital assets using yolov8
publisher IEEE
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41502/1/Real-Time_Object_Detection_System_for_Hospital_Assets_Using_YOLOv8.pdf
http://umpir.ump.edu.my/id/eprint/41502/
https://doi.org/10.1109/ICE3IS62977.2024.10775948
_version_ 1822924942579073024
score 13.234276

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