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Open AccessArticle

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

1
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
2
Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
3
College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar 31001, Iraq
4
eVIDA Lab, University of Deusto, 48007 Bilbao, Spain
5
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
*
Authors to whom correspondence should be addressed.
Diagnostics 2021, 11(1), 105; https://doi.org/10.3390/diagnostics11010105
Received: 16 December 2020 / Revised: 4 January 2021 / Accepted: 8 January 2021 / Published: 11 January 2021
The 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. View Full-Text
Keywords: anterior cruciate ligament; healthcare; knee injury; artificial intelligence; convolutional neural network; MRI; detection; classification; residual network; augmentation anterior cruciate ligament; healthcare; knee injury; artificial intelligence; convolutional neural network; MRI; detection; classification; residual network; augmentation
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MDPI and ACS Style

Javed Awan, M.; Mohd Rahim, M.S.; Salim, N.; Mohammed, M.A.; Garcia-Zapirain, B.; Abdulkareem, K.H. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics 2021, 11, 105. https://doi.org/10.3390/diagnostics11010105

AMA Style

Javed Awan M, Mohd Rahim MS, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics. 2021; 11(1):105. https://doi.org/10.3390/diagnostics11010105

Chicago/Turabian Style

Javed Awan, Mazhar; Mohd Rahim, Mohd S.; Salim, Naomie; Mohammed, Mazin A.; Garcia-Zapirain, Begonya; Abdulkareem, Karrar H. 2021. "Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach" Diagnostics 11, no. 1: 105. https://doi.org/10.3390/diagnostics11010105

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