期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 41, 期 6, 页码 7603-7614出版社
IOS PRESS
DOI: 10.3233/JIFS-212015
关键词
Magnetic resonance imaging; ResNet50; MultiResUNet; Sparse aware noise reduction Convolutional neural network (SANR CNN); Adam optimizer
Research developed an intelligent system based on deep learning to accurately predict the severity of knee rheumatoid arthritis from knee data. This method improved the existing evaluation methods with high accuracy and efficiency.
Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据