Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/A5DF1B62-C7D1-480E-A572-3FA35330DBEE | ||
| Έτος | 2021 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | K. Giannakaki, K. Moirogiorgou, M. Zervakis, N. Anousakis-Vlachochristou, G. K. Matsopoulos, C. Komporozos, V. Sourides, G. Katsimagklis, M. Drakopoulou, K. Toutouzas, C. Avgeropoulou and A. Androulakis, "Automatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognition," presented at the 2021 IEEE International Conference on Imaging Systems and Techniques (IST), Kaohsiung, Taiwan, 2021, doi: 10.1109/IST50367.202 https://doi.org/10.1109/IST50367.2021.9651398 | ||
| Εμφανίζεται στις Συλλογές |
Automatic methods for heart disease recognition are a promising asset in precise diagnosis and prevention of complications. Regarding bicuspid aortic valve, for which this field is still limited, accurate aortic valve detection would be an essential step in the procedure of using the most common testing method, echocardiography, to automatically detect this malformation. In this study, we propose using a convolutional neural network with U-net architecture for demarcating the aortic valve area in echocardiography images, as an initial step in automatic bicuspid aortic valve detection. Our model achieved a prediction accuracy of 97%, sensitivity 94%, specificity 98% and Intersection over Union 87%.
Copyright © DIAS 2013
