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 |
|---|---|
| Pages (from-to) | 1358-1365+11 |
| Journal | IEEJ Transactions on Electronics, Information and Systems |
| Volume | 127 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2007 Jan 1 |
Keywords
- Cirrhosis
- Discrete wavelet transformed
- Pyramid recurrent neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
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Cite this
Lu, J., Liu, J., Zhao, X., & Yahagi, T. (2007). Ultrasonographic diagnosis of cirrhosis based on preprocessing using pyramid recurrent neural network. IEEJ Transactions on Electronics, Information and Systems, 127(9), 1358-1365+11. https://doi.org/10.1541/ieejeiss.127.1358