Incorporating the Laplacian Filter with a Three-Stream Multi-Channel Convolutional Neural Network for Improved Abnormality Detection in Knee MRIs

Abstract

AbstractDespite ACL and meniscus tears being among the most common movement induced injuries, they are often the most difficult to diagnose due to the variable severity with which these tears occur. Typically, magnetic resonance imaging (MRI) scans are used for diagnosing ligament tears, but performing and analyzing these scans is time consuming and expensive due to the necessitation of a radiologist or professional orthopedic specialist. Consequently, we developed a custom three-stream convolutional neural network (CNN) architecture that contains multiple channels to automate the diagnosis of ACL and meniscus tears from MRI scans. Our algorithm utilizes the sagittal, coronal, and axial slices to maximize feature extraction. Furthermore, we apply the Laplace Operator on the MRI scan images to evaluate and compare its propensity in different medical imaging modalities. The algorithm attained an accuracy of 92.80%, significantly higher than that of orthopedic diagnosis accuracy. Our results point towards the feasibility of shallow, multi-channel CNNs and the ability of the Laplace Operator to improve performance metrics for MRI scan diagnosis.