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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 745-749
Zhiping Xu , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
Xiang Liu , School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
Xi En Cheng , Jingdezhen Ceramic Institute, Jingdezhen, China
Jia Lin Song , Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China
Jian Quan Zhang , Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China
ABSTRACT
This paper proposes a novel fully automatic diagnosis method for liver cirrhosis based on the reading of high-frequency ultrasound images. The proposed method determines the cirrhosis stage via a deep-learning neural network. First, we feed an ultrasound image into an autoencoder to generate the capsule-enhanced version of the image and binarize the enhanced image. Then, we employ a partition-clustering algorithm to obtain the top-end largest-area partition cluster, which represents the upper layer of the liver, and thereby locate the final liver capsule based on least-squares polynomial fitting. After separating the parenchymal region from the image, we use the proposed residual neural network to determine the cirrhosis stage. Experimental results demonstrate the high accuracy and effectiveness of the proposed method, which outperforms five other state-of-the-art methods. The proposed method is expected to improve the efficiency and accuracy of the clinical diagnosis of liver cirrhosis.
INDEX TERMS
Liver, Ultrasonic imaging, Neural networks, Kernel, Partitioning algorithms, Clustering algorithms, Indexes
CITATION

Z. Xu, X. Liu, X. E. Cheng, J. L. Song and J. Q. Zhang, "Diagnosis of cirrhosis stage via deep neural network," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 745-749.
doi:10.1109/BIBM.2017.8217748
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