Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI)

被引:8
作者
Esfandiari, Mohammad Amin [1 ]
Tafti, Mohammad Fallah [1 ]
Dabanloo, Nader Jafarnia [2 ]
Yousefirizi, Fereshteh [3 ]
机构
[1] Islamic Azad Univ, Dept Biomed Engn, South Tehran Branch, Tehran, Iran
[2] Islamic Azad Univ, Dept Biomed Engn, Sci & Res Branch, Tehran, Iran
[3] BC Canc Res Inst, Dept Integrat Oncol, Vancouver, BC, Canada
关键词
Convolutional neural network; Deep learning; Rotator cuff tear; MRI; TEXTURE ANALYSIS; SUPRASPINATUS; SEGMENTATION;
D O I
10.1016/j.heliyon.2023.e15804
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The rotator cuff tear is a common situation for basketballers, handballers, or other athletes that strongly use their shoulders. This injury can be diagnosed precisely from a magnetic resonance (MR) image. In this paper, a novel deep learning-based framework is proposed to diagnose rotator cuff tear from MRI images of patients suspected of the rotator cuff tear. First, we collected 150 shoulders MRI images from two classes of rotator cuff tear patients and healthy ones with the same numbers. These images were observed by an orthopedic specialist and then tagged and used as input in the various configurations of the Convolutional Neural Network (CNN). At this stage, five different configurations of convolutional networks have been examined. Then, in the next step, the selected network with the highest accuracy is used to extract the deep features and classify the two classes of rotator cuff tear and healthy. Also, MRI images are feed to two quick pre-trained CNNs (MobileNetv2 and SqueezeNet) to compare with the proposed CNN. Finally, the evaluation is performed using the 5-fold cross-validation method. Also, a specific Graphical User Interface (GUI) was designed in the MATLAB environment for simplicity, which allows for testing by detecting the image class. The proposed CNN achieved higher accuracy than the two mentioned pre-trained CNNs. The average accuracy, precision, sensitivity, and specificity achieved by the best selected CNN configuration are equal to 92.67%, 91.13%, 91.75%, and 92.22%, respectively. The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder MRI.
引用
收藏
页数:12
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