Automated Diagnosis of Cardiovascular Disease Through Measurement of Intima Media Thickness Using Deep Neural Networks
| Autor: | C. Rajasekaran, Ramani Kuchelar, Sudha S, Jayanthi K B |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Carotid Artery Common Carotid arteries Feature extraction 01 natural sciences Convolutional neural network Carotid Intima-Media Thickness 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Region of interest 0103 physical sciences medicine Humans Segmentation 010301 acoustics Ultrasonography business.industry Ultrasound Pattern recognition Image segmentation Ultrasonic imaging medicine.anatomical_structure Carotid Arteries Intima-media thickness Cardiovascular Diseases Artificial intelligence Neural Networks Computer business Artery |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Ultrasound images(US) of carotid artery aid in the detection and diagnosis of Cardiovascular Diseases (CVD). Traditional methods for analysis of US images employ hand crafted features to classify images, which need expert knowledge for careful design and lack robustness to variations, leading to low sensitivity in clinical applications. Intima Media Thickness (IMT) and elasticity are the predominant markers used for carotid artery (CA) atherosclerotic plaque detection. This paper proposes to address the problem by building Convolutional Neural Network (CNN) for segmentation of intima media complex (ie) Region of Interest (RoI). A dataset consisting of 450 subjects is used to train and validate the proposed CNN. Segmentation is done in the far wall region of the artery from the longitudinal B-mode images enabling atleast 24 RoIs and RoNIs (Region of Non Interest) for each image. The result of 10-fold cross validation shows accuracy of 99.54%. Mean deviation of IMT from manual tracings is found to be 0.06645mm. |
| Databáze: | OpenAIRE |
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