Citation

Thangaperumal, Sivashankari and Murugan, Pallikonda Rajasekaran and Hossen, Md. Jakir and Wong, Wai Kit and Ng, Poh Kiat (2025) Classification of anterior cruciate ligament tears in knee magnetic resonance images using pre-trained model and custom model. Scientific Reports, 15 (1). p. 13. ISSN 2045-2322

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Abstract

An anterior cruciate ligament (ACL) tear is a prevalent knee injury among athletes, and aged people with osteoporosis are at increased risk for it. For early detection and treatment, precise and rapid identification of ACL tears is significant. A fully automated system that can identify ACL tear is necessary to aid healthcare providers in determining the nature of injuries detected on Magnetic Resonance Imaging (MRI) scans. Two Convolutional Neural Networks (CNN), the pretrained model and the CustomNet model are trained and tested using 581 MRI scans of the knee. Feature extraction is done with the pre-trained ResNet-18 model, and the ISOMAP algorithm is used in the CustomNet model. Linear and nonlinear dimensionality reduction techniques are employed to extract the needed features from the image. For the ResNet-18 model, the accuracy rate ranges between 86% and 92% for various data partitions. After performing PCA, the improved classification rate ranges between 92% and 96.2%. The CustomNet model’s accuracy rate ranges from 40 to 70%, 70–90%, 60–70%, and 50–70% for different hyperparameter ensembles. Five-fold cross validation is implemented in CustomNet and it achieved an overall accuracy of 85.6%. These two models demonstrate superior efficiency and accuracy in classifying normal and ACL torn Knee MR images.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural networks (CNN), CustomNet, Feature extraction, ISOMAP, Pretrained model, Principal component analysis (PCA)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science R Medicine > RC Internal medicine
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	06 Nov 2025 06:30
Last Modified:	07 Nov 2025 01:13
URII:	http://shdl.mmu.edu.my/id/eprint/14719

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