Meniscal Tear and ACL Injury Detection Model Based on AlexNet and Iterative ReliefF

Abstract

Magnetic resonance (MR) is one of the special imaging techniques used to diagnose orthopedics and traumatology. In this study, a new method has been proposed to detect highly accurate automatic meniscal tear and anterior cruciate ligament (ACL) injuries. In this study, images in three different slices were collected. These are the sagittal, coronal, and axial slices, respectively. Images taken from each slice were categorized in 3 different ways: sagittal database (sDB), coronal database (cDB), and axial database (aDB). The proposed model in the study uses deep feature extraction. In this context, deep features have been obtained by using fully-connected layers of AlexNet architecture. In the second stage of the study, the most significant features were selected using the iterative RelifF (IRF) algorithm. In the last step of the application, the features are classified by using the k-nearest neighbor (kNN) method. Three datasets were used in the study. These datasets, sDB, and cDB, have four classes and consist of 442 and 457 images, respectively. The aDB used in the study has two class labels and consists of 190 images. The model proposed within the scope of the study was applied in 3 datasets. In this context, 98.42%, 100%, and 100% accuracy values were obtained for sDB, cDB, and aDB datasets, respectively. The study results showed that the proposed method detected meniscal tear and anterior cruciate ligament (ACL) injuries with high accuracy.