Analysis of framework networks for sign detection in deep learning models

Abstract

This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems. This work is devoted to different types of feature detection framework networks for machine learning architectures that perform magnetic resonance imaging (MRI) image classification of the knee. The resulting models were evaluated on the MRNet validation dataset, calculating the metrics (ROC-AUC), prediction accuracy, F1 score, and Cohen’s K-Kappa. The results of this work also show that Cohen's Kappa metric is important for evaluating models on the MRNet architecture because it provides a deeper understanding of the classification decisions of each model.