A ResNet50-Based Approach to Detect Multiple Types of Knee Tears Using MRIs

Abstract

The recommended tool for assessing knee injury is magnetic resonance imaging (MRI). However, knee MRI interpretation takes time and is vulnerable to clinical errors and inconsistency. A deep learning automated technique for reading knee MRI might help physicians identify high-risk patients and make diagnosis easier. In this study, we have proposed a deep learning-based model to detect ACL and meniscus tears and other knee abnormalities. At its core, this model is based on the ResNet50 transfer learning technique. In this paper, we have focused to present a ResNet50-based model for detecting different knee problems using MRIs. The best models for every option achieved the objectives that were probably similar. The models were developed using 18, 3, and 1 slice. These models’ outcomes were rather startling. The AUC findings obtained with 1 slice per MRI exam were equivalent to those obtained with 18 and 3 slices and, in some cases, were significantly better. The dataset used in this model is from Stanford University. We trained this model in three different settings of MRI slices (18, 3, and 1). The best results that our models were able to achieve were when trained using 3 slices of each MRI sample. The area under the receiver operating characteristic curve, or AUC curve, values that our best models were able to achieve for detecting ACL, meniscus, and other knee abnormalities are 0.87, 0.82, and 0.90, respectively. The results of our models are comparable to some state-of-the-art models. These models are very fast and efficient to train and hence will be helpful to doctors for making an effective and fast diagnosis based on knee MRIs.