Publication:
Stacking Ensemble for Pill Image Classification

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dc.citedby1
dc.contributor.authorAhammed F.A.A.B.S.en_US
dc.contributor.authorMohanan V.en_US
dc.contributor.authorYeo S.F.en_US
dc.contributor.authorJothi N.en_US
dc.contributor.authorid59224694800en_US
dc.contributor.authorid36069451500en_US
dc.contributor.authorid56489745300en_US
dc.contributor.authorid54928769700en_US
dc.date.accessioned2025-03-03T07:46:53Z
dc.date.available2025-03-03T07:46:53Z
dc.date.issued2024
dc.description.abstractMedication errors, commonly contributed by human factors, have the potential to cause serious harm to human beings. Therefore, a deep learning-based approach is necessary to be developed to ensure patient safety. The investigation involves three core base models?ResNet50, InceptionV3, and MobileNet?assessing individual performances. A novel stacking ensemble method was proposed, and its efficacy is compared to the base models and related works. The research?s key findings reveal that the proposed stacking ensemble model outperforms all the other models with a 98.80% test accuracy. It also excels in precision, recall, and F1-score, with scores of 98.81%, 98.80%, and 98.80%, respectively. The study also indicates the time efficiency of the proposed stacking ensemble compared to other methods. Notably, MobileNet exhibits superiority in training and prediction time, emphasizing the trade-offs between accuracy and efficiency. Overall, this research sheds light on the overlooked potential of ensemble methods in pill image classification, contributing a robust solution to enhance our understanding of their effectiveness in healthcare and pharmaceutical applications. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-62881-8_8
dc.identifier.epage99
dc.identifier.scopus2-s2.0-85198911019
dc.identifier.spage90
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85198911019&doi=10.1007%2f978-3-031-62881-8_8&partnerID=40&md5=17f6500e06ff66ef532ae50c9d73ebb3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37042
dc.identifier.volume1036 LNNS
dc.pagecount9
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectDeep learning
dc.subjectEconomic and social effects
dc.subjectEfficiency
dc.subjectImage enhancement
dc.subjectLearning systems
dc.subjectBase models
dc.subjectEnsemble methods
dc.subjectHuman being
dc.subjectImages classification
dc.subjectLearning-based approach
dc.subjectMachine-learning
dc.subjectMedication errors
dc.subjectPatient safety
dc.subjectPill classification
dc.subjectStackings
dc.subjectImage classification
dc.titleStacking Ensemble for Pill Image Classificationen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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