Published July 13, 2021
| Version v1
Poster
Open
DEEP LEARNING METHODS FOR PLAQUE TYPE CLASSIFICATION BASED ON THE US IMAGES OF CAROTID ARTERY
Authors/Creators
- 1. Faculty of Science, University of Kragujevac, Serbia, Radoja Domanovica 12, 34000 Kragujevac, Serbia; BioIRC, Bioengineering Research and Development Center, Prvoslava Stojanovica 6, 34000 Kragujevac
- 2. Faculty of Engineering University of Kragujevac; Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- 3. Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia; Institute of Information Technologies, Kragujevac, Serbia
- 4. Faculty of Medicine, University of Belgrade, Serbia
Description
Carotid atherosclerotic plaque deposition leads to arterial stenosis and severe catastrophic events over time (stroke and Transitional Ischemic Attack (TIA)). It is well established that the tissue composition plays a central role for the stability and vulnerability of atherosclerotic plaques. Identification of atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease and stratify them as a high/low risk. The main aim of this study was to identify the plaque types such as lipid, fibrous and calcified tissue, by applying the deep learning methods on US images.
Files
ESB2021_Abstract_Arsic et al.pdf
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