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

Jianming Lu*, Jiang Liu, Xueqin Zhao, Takashi Yahagi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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 four types according to the size of hypoechoic nodular lesions. The B-mode patterns are wavelet transformed, and then the compressed data are fed into a pyramid neural network to diagnose the type of cirrhotic diseases. Compared with the three-layer neural networks, the performance of the proposed pyramid recurrent neural network is improved by a more efficient utilization of lower layers. Simulation results show that the proposed system is suitable for diagnosis of cirrhosis diseases.

Original languageEnglish
Pages (from-to)10-19
Number of pages10
JournalElectronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number7
Publication statusPublished - 2008


  • Cirrhosis
  • Discrete wavelet transform
  • Pyramid recurrent neural network

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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