Classification of rotator cuff tears in ultrasound images using deep learning models.
Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algorithms are increasingly used to analyze medical im...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 5; pp. 1269 - 1279 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156318881&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156318881 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2022 vid: 60 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 156318881 156318881 NLM35043367 10.1007/s11517-022-02502-6 NLM35043367 156318881 ppf: 1269 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of rotator cuff tears in ultrasound images using deep learning models. aug: au: Ho, Thao Thi Kim, Geun-Tae Kim, Taewoo Choi, Sanghun Park, Eun-Kee affil: School of Mechanical Engineering, College of Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, 41566, Daegu, Republic of Korea sug: subj: Magnetic Resonance Imaging Ultrasonography ab: Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algorithms are increasingly used to analyze medical images, but they have not been used to identify RCTs with ultrasound images. The aim of this study is to develop an approach to automatically classify RCTs and provide visualization of tear location using ultrasound images and convolutional neural networks (CNNs). The proposed method was developed using transfer learning and fine-tuning with five pre-trained deep models (VGG19, InceptionV3, Xception, ResNet50, and DenseNet121). The Bayesian optimization method was also used to optimize hyperparameters of the CNN models. A total of 194 ultrasound images from Kosin University Gospel Hospital were used to train and test the CNN models by five-fold cross-validation. Among the five models, DenseNet121 demonstrated the best classification performance with 88.2% accuracy, 93.8% sensitivity, 83.6% specificity, and AUC score of 0.832. A gradient-weighted class activation mapping (Grad-CAM) highlighted the sensitive features in the learning process on ultrasound images. The proposed approach demonstrates the feasibility of using deep learning and ultrasound images to assist RCTs' diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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