Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning.
Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision...
| Published in: | Journal of Medical Systems Vol. 43; no. 8 |
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| Main Authors: | , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137490063&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490063 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490063 137490063 137490063 10.1007/s10916-019-1406-2 137490063 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning. aug: au: Savaş, Serkan Topaloğlu, Nurettin Kazcı, Ömer Koşar, Pınar Nercis affil: Faculty of Technology, Computer Engineering Department Ph.D, Gazi University, Ankara, Turkey sug: subj: Carotid Artery Diseases Complications Stroke Atherosclerosis Diagnosis Early Diagnosis Methods Carotid Intima-Media Thickness Classification Ultrasonography Methods Human Research Methodology Artificial Intelligence Methods Deep Learning Algorithms Neural Pathways Diagnostic Imaging Classification Validity Sensitivity and Specificity ab: Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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