Open Access
ARTICLE
Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning
Yun Tan1, #, Ling Tan2, #, Xuyu Xiang1, *, Hao Tang2, *, Jiaohua Qin1, Wenyan Pan1
1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410114, China.
2 The Second Xiangya Hospital of Central South University, Changsha, 410011, China.
* Corresponding Authors: Xuyu Xiang. Email: ;
Hao Tang. Email: .
# The authors contributed equally to this work.
Computers, Materials & Continua 2020, 62(3), 1201-1215. https://doi.org/10.32604/cmc.2020.07127
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.
Keywords
Cite This Article
APA Style
Tan, Y., Tan, L., Xiang, X., Tang, H., Qin, J. et al. (2020). Automatic detection of aortic dissection based on morphology and deep learning. Computers, Materials & Continua, 62(3), 1201-1215. https://doi.org/10.32604/cmc.2020.07127
Vancouver Style
Tan Y, Tan L, Xiang X, Tang H, Qin J, Pan W. Automatic detection of aortic dissection based on morphology and deep learning. Comput Mater Contin. 2020;62(3):1201-1215 https://doi.org/10.32604/cmc.2020.07127
IEEE Style
Y. Tan, L. Tan, X. Xiang, H. Tang, J. Qin, and W. Pan "Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning," Comput. Mater. Contin., vol. 62, no. 3, pp. 1201-1215. 2020. https://doi.org/10.32604/cmc.2020.07127
Citations