@Article{cmc.2020.07127,
AUTHOR = {Yun Tan, Ling Tan, Xuyu Xiang, Hao Tang, Jiaohua Qin, Wenyan Pan},
TITLE = {Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1201--1215},
URL = {http://www.techscience.com/cmc/v62n3/38349},
ISSN = {1546-2226},
ABSTRACT = {Aortic dissection (AD) is a kind of acute and rapidly progressing 
cardiovascular disease. In this work, we build a CTA image library with 88 CT cases, 43
cases of aortic dissection and 45 cases of health. An aortic dissection detection method 
based on CTA images is proposed. ROI is extracted based on binarization and 
morphology opening operation. The deep learning networks (InceptionV3, ResNet50, and
DenseNet) are applied after the preprocessing of the datasets. Recall, F1-score, Matthews 
correlation coefficient (MCC) and other performance indexes are investigated. It is 
shown that the deep learning methods have much better performance than the traditional
method. And among those deep learning methods, DenseNet121 can exceed other 
networks such as ResNet50 and InceptionV3.},
DOI = {10.32604/cmc.2020.07127}
}