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 language | English |
|---|---|
| Journal | IEEJ Transactions on Electronics, Information and Systems |
| Volume | 127 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2007 |
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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 journal › Article
}
TY - JOUR
T1 - Ultrasonographic diagnosis of cirrhosis based on preprocessing using pyramid recurrent neural network
AU - Lu, Jianming
AU - Liu, Jiang
AU - Zhao, Xueqin
AU - Yahagi, Takashi
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Cirrhosis
KW - Discrete wavelet transformed
KW - Pyramid recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=34548810210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548810210&partnerID=8YFLogxK
U2 - 10.1541/ieejeiss.127.1358
DO - 10.1541/ieejeiss.127.1358
M3 - Article
AN - SCOPUS:34548810210
VL - 127
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 9
ER -