Abstract
Magnetic resonance imaging (MRI) is used by surgeons to analyse different tears in the knee part of the body. This technique has demonstrated significant accuracy for the diagnosis of injuries like meniscus tears, ligament injuries, ACL tears, etc. However, studying these MRI manually is very time-consuming and has high chances of wrong prediction. It can take more than thirty minutes to properly examine a knee MRI and come to a result. It is a time-intensive process and has high error probability. Therefore, an automated model for examining knee images to predict the tears would cut the time cost and also the human errors. Generally, deep learning models work best with large amounts of dataset. But, there is not much data present out there of knee injuries which can help to train the model properly. For this purpose, we decided to analyse different deep learning and machine learning algorithms to compare and find the most efficient method. These models were applied on knee MRI dataset for the prediction of anterior cruciate ligament (ACL) tear from sagittal plane MRI scans which provide scans of totally ruptured, partially injured and healthy knees. After our analysis of various models, it was found that the best results were given by Support Vector Machine (SVM) followed by Convolutional Neural Network (CNN) on the masked dataset.
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Kapoor, V., Tyagi, N., Manocha, B., Arora, A., Roy, S., Nagrath, P. (2021). Detection of Anterior Cruciate Ligament Tear Using Deep Learning and Machine Learning Techniques. In: Khanna, A., Gupta, D., Pólkowski, Z., Bhattacharyya, S., Castillo, O. (eds) Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_2
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DOI: https://doi.org/10.1007/978-981-15-8335-3_2
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