Journal
DIAGNOSTICS
Volume 11, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics11010105
Keywords
anterior cruciate ligament; healthcare; knee injury; artificial intelligence; convolutional neural network; MRI; detection; classification; residual network; augmentation
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The study proposed a deep learning method for early detection of ACL injury via automatic MRI, achieving high accuracy and evaluation metrics. It utilized a ResNet-14 CNN architecture with class balancing and data augmentation, showing differentiation and evaluation results for healthy tear, partial tear, and fully ruptured tear.
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.
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