Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning
| Autor: | Ömer Kazcı, Serkan Savaş, Pınar Koşar, Nurettin Topaloğlu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
020205 medical informatics
Computer science Carotid arteries Medicine (miscellaneous) Early detection Health Informatics 02 engineering and technology Carotid Intima-Media Thickness Convolutional neural network Deep Learning Health Information Management Artificial Intelligence Carotid artery disease 0202 electrical engineering electronic engineering information engineering medicine Humans cardiovascular diseases Ultrasonography Contextual image classification business.industry Deep learning Ultrasound Pattern recognition Decision Support Systems Clinical medicine.disease Carotid Arteries Intima-media thickness cardiovascular system Artificial intelligence business Algorithms Information Systems |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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