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dc.contributor.authorAzian Azamimi, Abdullah
dc.contributor.authorNur Siti Fatimah Azz-Zahra, Md Som
dc.date.accessioned2014-03-06T06:58:04Z
dc.date.available2014-03-06T06:58:04Z
dc.date.issued2013
dc.identifier.citationApplied Mechanics and Materials, vol. 339, 2013, pages 219-224en_US
dc.identifier.isbn978-303785737-3
dc.identifier.issn1660-9336
dc.identifier.urihttp://www.scientific.net/AMM.339.219
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/32391
dc.descriptionLink to publisher's homepage at http://www.ttp.net/en_US
dc.description.abstractKnee injuries is quite common in sport injuries and one of the most frequent happen are anterior cruciate ligament (ACL) knee injuries. There are two types of ACL injuries which are partial tear and complete tear. Currently physical tests, MRI image interpretation from expert and arthroscopy method are used to diagnoses the injuries. These procedures somehow are time consuming, invasive and operator dependent. To overcome these limitations, the intelligent diagnostic system using artificial neural network (ANN) has been proposed as an alternate way to give an early detection and also to classify the types of ACL injuries. We have used BP ANN and k-NN for the classification purpose. From these both classifiers, BP ANN give the higher accuracy which is 94.44% compared to k-NN classifier which the highest accuracy only up to 87.8333%.en_US
dc.language.isoenen_US
dc.publisherTrans Tech Publicationsen_US
dc.subjectACLen_US
dc.subjectDiagnostic systemen_US
dc.subjectKnee injuriesen_US
dc.subjectMri imagesen_US
dc.subjectNeural networken_US
dc.titleDesign of an intelligent diagnostic system for detection of knee injuriesen_US
dc.typeArticleen_US
dc.contributor.urlazamimi@unimap.edu.myen_US
dc.contributor.urlfatimah_azzzahra@yahoo.comen_US


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