Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach

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dc.contributor.authorAwan, Mazhar Javed
dc.contributor.authorMohd Rahim, Mohd Shafry
dc.contributor.authorSalim, Naomie
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.date.accessioned2025-08-13T06:53:12Z
dc.date.available2025-08-13T06:53:12Z
dc.date.issued2021-01-11
dc.date.updated2025-08-13T06:53:12Z
dc.description.abstractThe most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.en
dc.identifier.citationAwan, M. J., Rahim, M. S. M., Salim, N., Mohammed, M. A., Garcia-Zapirain, B., & Abdulkareem, K. H. (2021). Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach. Diagnostics, 11(1). https://doi.org/10.3390/DIAGNOSTICS11010105
dc.identifier.doi10.3390/DIAGNOSTICS11010105
dc.identifier.eissn2075-4418
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3358
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2021 by the authors
dc.subject.otherAnterior cruciate ligament
dc.subject.otherArtificial intelligence
dc.subject.otherAugmentation
dc.subject.otherClassification
dc.subject.otherConvolutional neural network
dc.subject.otherDetection
dc.subject.otherHealthcare
dc.subject.otherKnee injury
dc.subject.otherMRI
dc.subject.otherResidual network
dc.titleEfficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approachen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleDiagnostics
oaire.citation.volume11
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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