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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41502/ http://umpir.ump.edu.my/id/eprint/41502/1/Real-Time_Object_Detection_System_for_Hospital_Assets_Using_YOLOv8.pdf |
| _version_ | 1848826353346936832 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:43:28Z |
| format | Conference or Workshop Item |
| id | ump-41502 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:43:28Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | Real-Time Object Detection System for Hospital Assets Using YOLOv8 |
| title_sort | real-time object detection system for hospital assets using yolov8 |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/41502/ http://umpir.ump.edu.my/id/eprint/41502/ http://umpir.ump.edu.my/id/eprint/41502/1/Real-Time_Object_Detection_System_for_Hospital_Assets_Using_YOLOv8.pdf |