Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

内膜中层厚度 颈动脉 卷积神经网络 超声波 深度学习 医学 放射科 人工智能 人工神经网络 颈总动脉 模式识别(心理学) 机器学习 计算机科学 心脏病学
作者
Serkan Savaş,Nurettin Topaloğlu,Ömer Kazcı,Pınar Nercis Koşar
出处
期刊:Journal of Medical Systems [Springer Science+Business Media]
卷期号:43 (8) 被引量:43
标识
DOI:10.1007/s10916-019-1406-2
摘要

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.
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