Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging

Awan, Mazhar Javed, Rahim, Mohd Shafry Mohd, Salim, Naomie, Rehman, Amjad, Nobanee, Haitham and Shabir, Hassan (2021) Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging. JOURNAL OF PERSONALIZED MEDICINE, 11 (11). 1163-.

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Official URL: http://dx.doi.org/10.3390/jpm11111163

Abstract

Anterior cruciate ligament (ACL) tear is caused by partially or completely torn ACL ligament in the knee, especially in sportsmen. There is a need to classify the ACL tear before it fully ruptures to avoid osteoarthritis. This research aims to identify ACL tears automatically and efficiently with a deep learning approach. A dataset was gathered, consisting of 917 knee magnetic resonance images (MRI) from Clinical Hospital Centre Rijeka, Croatia. The dataset we used consists of three classes: non-injured, partial tears, and fully ruptured knee MRI. The study compares and evaluates two variants of convolutional neural networks (CNN). We first tested the standard CNN model of five layers and then a customized CNN model of eleven layers. Eight different hyper-parameters were adjusted and tested on both variants. Our customized CNN model showed good results after a 25% random split using RMSprop and a learning rate of 0.001. The average evaluations are measured by accuracy, precision, sensitivity, specificity, and F1-score in the case of the standard CNN using the Adam optimizer with a learning rate of 0.001, i.e., 96.3%, 95%, 96%, 96.9%, and 95.6%, respectively. In the case of the customized CNN model, using the same evaluation measures, the model performed at 98.6%, 98%, 98%, 98.5%, and 98%, respectively, using an RMSprop optimizer with a learning rate of 0.001. Moreover, we also present our results on the receiver operating curve and area under the curve (ROC AUC). The customized CNN model with the Adam optimizer and a learning rate of 0.001 achieved 0.99 over three classes was highest among all. The model showed good results overall, and in the future, we can improve it to apply other CNN architectures to detect and segment other ligament parts like meniscus and cartilages.

Item Type:	Article
Uncontrolled Keywords:	anterior cruciate ligament, osteoarthritis, deep learning, classification, public health, healthcare, diagnosis, convolutional neural network, knee bone, radiographic image analysis, human and health
Divisions:	Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures
Depositing User:	Symplectic Admin
Date Deposited:	21 Jan 2022 16:59
Last Modified:	18 Jan 2023 21:15
DOI:	10.3390/jpm11111163
Open Access URL:	https://www.mdpi.com/2075-4426/11/11/1163
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3147365