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Sensors 2017, 17(1), 149; doi:10.3390/s17010149

Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

1
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China
2
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
3
Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai 200003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Xiaoning Jiang
Received: 20 November 2016 / Revised: 27 December 2016 / Accepted: 10 January 2017 / Published: 13 January 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2911 KB, uploaded 13 January 2017]   |  

Abstract

This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images. View Full-Text
Keywords: ultrasound imaging; computer-aided diagnosis; cirrhosis; convolutional neural network ultrasound imaging; computer-aided diagnosis; cirrhosis; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, X.; Song, J.L.; Wang, S.H.; Zhao, J.W.; Chen, Y.Q. Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification. Sensors 2017, 17, 149.

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