Ultrasonographic diagnosis of cirrhosis based on preprocessing using pyramid recurrent neural network

Jianming Lu, Jiang Liu, Xueqin Zhao, Takashi Yahagi

Research output: Contribution to journalArticle

Abstract

In this paper, a pyramid recurrent neural network is applied to characterize the hepatic parenchymal diseases in ultrasonic B-scan texture. The cirrhotic parenchymal diseases are classified into 4 types according to the size of hypoechoic nodular lesions. The B-mode patterns are wavelet transformed, and then the compressed data are feed into a pyramid neural network to diagnose the type of cirrhotic diseases. Compared with the 3-layer neural networks, the performance of the proposed pyramid recurrent neural network is improved by utilizing the lower layer effectively. The simulation result shows that the proposed system is suitable for diagnosis of cirrhosis diseases.

Original languageEnglish
JournalIEEJ Transactions on Electronics, Information and Systems
Volume127
Issue number9
DOIs
Publication statusPublished - 2007

Fingerprint

Recurrent neural networks
Neural networks
Textures
Ultrasonics

Keywords

  • Cirrhosis
  • Discrete wavelet transformed
  • Pyramid recurrent neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Ultrasonographic diagnosis of cirrhosis based on preprocessing using pyramid recurrent neural network. / Lu, Jianming; Liu, Jiang; Zhao, Xueqin; Yahagi, Takashi.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 127, No. 9, 2007.

Research output: Contribution to journalArticle

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