REVIEW AND ANALYSIS OF BASIC FEATURE DETECTION NETWORKS FOR CLASSIFICATION OF MRI IMAGES IN DEEP LEARNING MODELS

Abstract

This paper presents an evaluation of modern deep learning models for the classification of MRI images of the knee joint. Among all the work related to this research, there have been several attempts to retrain the original MRNet model on more modern computer vision architectures. Also, no attempt has yet been reported to document the incremental improvement in MRNet prediction accuracy using newer computer vision architectures. This paper presents a comparative analysis of modern deep architectures of computer vision for extracting features from MRI images of the knee joint in the tasks of classification of injuries and anomalies of the knee. Such an analysis is needed, at least as a guide to creating applied architectures of machine learning models aimed at automated diagnosis of knee injuries in medical devices and systems. In the field of artificial intelligence, deep learning (DL) algorithms can be applied directly to many different musculoskeletal radiology tasks, including image reconstruction, synthetic imaging, tissue segmentation, and diagnosis and detection of musculoskeletal disease characteristics on radiographs, ultrasound , CT and MRI images. Ideally, such systems should also help radiologists focus on rare diseases as well as very complex abnormalities. At the same time, the task of automating the process of diagnosing typical injuries and anomalies is set. The level of confidence in the result of prediction should be similar to the conclusions of commissions of expert radiologists. To frame such a benchmarking analysis, this paper compares the performance of the basic MRNet architecture for the knee MRI image classification task, using various state-of-the-art computer vision architectures as framework networks for feature extraction. It also demonstrates a gradual increase in the prediction accuracy of these models in accordance with the evolution of the framework models themselves. A rather important aspect of the presented research is the fact that all machine learning models developed and trained in the considered experiment have a unified architecture, except for the feature extraction framework, and they were all trained from scratch using the same model parameters and training parameters. In addition, the model estimation strategies in this work use an additional metric that has not yet been measured and compared in any related work, namely Cohen’s Kappa metric. This metric is significant because the MRNet dataset used in this paper is not balanced.