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Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works

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dc.citedby0
dc.contributor.authorMoktar M.H.en_US
dc.contributor.authorHajjaj S.en_US
dc.contributor.authorMohamed H.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorid57215719975en_US
dc.contributor.authorid55812832600en_US
dc.contributor.authorid57136356100en_US
dc.contributor.authorid26326032700en_US
dc.date.accessioned2025-03-03T07:47:57Z
dc.date.available2025-03-03T07:47:57Z
dc.date.issued2024
dc.description.abstractThis paper evaluates the performance of modern AI-based object detection models that can be used for object classification and sorting applications. In this case, we focused on the classification of the medical waste for the current global situation which is the medical waste management during the post-pandemic of Covid-19 phase. A few classification models were used and compared between (1) CNN and ResNet50 and (2) YOLO v3 and YOLO v4. The results also were compared with the previous works that focused on waste classification. The difference between this work is the image dataset, which our work train and test the medical waste (facemask, glove, and syringe), while the previous works focused on general waste such as food, plastic, metal, paper, and others. From 2207 images of the medical waste, CNN and ResNet achieved 89.35 and 85.75% of accuracy, respectively, where it requires more images per class for the training improvement. YOLO v3 and YOLO v4 used 3073 images for training and achieved 84.86 and 89.21% of mean average precision (mAP). Our YOLO v3 mAP is in the average value among the previous works, while YOLO v4 has a higher mAP compared to the YOLO v4 training from other works. The YOLO v4 then was tested in real-time medical waste detection and successfully detected the masks, gloves, and syringe. However, there are still some wrong detections during the real-time detection using the camera, especially with other objects with similar shapes to the medical waste. Further, performance evaluations are required that can be used for medical waste objects and also for other different objects based on the applications. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-99-8498-5_40
dc.identifier.epage489
dc.identifier.scopus2-s2.0-85187777137
dc.identifier.spage475
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85187777137&doi=10.1007%2f978-981-99-8498-5_40&partnerID=40&md5=d5b6813ad0986a6435b2ae189906cb47
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37144
dc.identifier.volume845
dc.pagecount14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectImage enhancement
dc.subjectMedical imaging
dc.subjectObject detection
dc.subjectStatistical tests
dc.subjectWaste management
dc.subjectAI algorithms
dc.subjectDeep learning
dc.subjectDetection models
dc.subjectMedical wastes
dc.subjectObject classification
dc.subjectObject sorting
dc.subjectObjects detection
dc.subjectPerformance
dc.subjectPerformance reviews
dc.subjectWaste sorting
dc.subjectDeep learning
dc.titlePerformance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Worksen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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