Publication:
Medical Waste Detection and Classification Through YOLO Algorithms

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dc.citedby0
dc.contributor.authorMoktar M.H.B.en_US
dc.contributor.authorHajjaj S.S.H.en_US
dc.contributor.authorMohamed H.en_US
dc.contributor.authorid57215719975en_US
dc.contributor.authorid55812832600en_US
dc.contributor.authorid57136356100en_US
dc.date.accessioned2025-03-03T07:46:04Z
dc.date.available2025-03-03T07:46:04Z
dc.date.issued2024
dc.description.abstractGeneral 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. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-70687-5_3
dc.identifier.epage33
dc.identifier.scopus2-s2.0-85211356838
dc.identifier.spage22
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85211356838&doi=10.1007%2f978-3-031-70687-5_3&partnerID=40&md5=87c867523cb1288fab29d9a31fc17249
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36953
dc.identifier.volume1133 LNNS
dc.pagecount11
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectChemical wastes
dc.subjectDeep learning
dc.subjectAccidental injuries
dc.subjectArtificial intelligence methods
dc.subjectClassification models
dc.subjectClassifieds
dc.subjectDeep learning
dc.subjectMachine-learning
dc.subjectMedical wastes
dc.subjectObjects detection
dc.subjectPerformances evaluation
dc.subjectWaste classification
dc.subjectSyringes
dc.titleMedical Waste Detection and Classification Through YOLO Algorithmsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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