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Ultrasonic diagnosis of cirrhosis based on preprocessing using pyramid recurrent neural network
Author(s) -
Lu Jianming,
Liu Jiang,
Zhao Xueqin,
Yahagi Takashi
Publication year - 2008
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10121
Subject(s) - pyramid (geometry) , preprocessor , artificial neural network , cirrhosis , computer science , artificial intelligence , pattern recognition (psychology) , texture (cosmology) , parenchyma , ultrasonic sensor , wavelet , image (mathematics) , radiology , medicine , pathology , mathematics , geometry
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. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(7): 10–19, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecj.10121