Classification of liver diseases from CT images using BP-CMAC neural network

Chien Cheng Lee, Pau-Choo Chung, Yieng Jair Chen

研究成果: Paper同行評審

15 引文 斯高帕斯(Scopus)

摘要

In this paper, a novel BP-CMAC neural network classifier for the classification of liver diseases is proposed. The BP-CMAC neural network takes the advantages of the back-propagation (BP) and CMAC networks. It utilities the CMAC to simplify the input space and forwards to the BP network as inputs. Therefore, it can reduce the memory allocation for CMAC network, and speed up the learning process. The BP-CMAC is used to construct the liver disease diagnosis system for testing the liver cyst, hepatoma, and cavernous hemagioma. The overall distinction rate is about 87% even though the symptoms of hepatoma and cavernous hemagioma are very similar.

原文English
頁面118-121
頁數4
出版狀態Published - 2005 10月 31
事件9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA - Hsinchu, Taiwan
持續時間: 2005 5月 282005 5月 30

Other

Other9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA
國家/地區Taiwan
城市Hsinchu
期間05-05-2805-05-30

All Science Journal Classification (ASJC) codes

  • 軟體

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