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

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

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

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

Institute of Computer and Communication Engineering

Research output: Contribution to conference › Paper › peer-review

15 Citations (Scopus)

Abstract

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.

Original language	English
Pages	118-121
Number of pages	4
Publication status	Published - 2005 Oct 31
Event	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA - Hsinchu, Taiwan Duration: 2005 May 28 → 2005 May 30

Other

Other	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA
Country/Territory	Taiwan
City	Hsinchu
Period	05-05-28 → 05-05-30

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title = "Classification of liver diseases from CT images using BP-CMAC neural network",

abstract = "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.",

author = "Lee, {Chien Cheng} and Pau-Choo Chung and Chen, {Yieng Jair}",

year = "2005",

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note = "9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA ; Conference date: 28-05-2005 Through 30-05-2005",

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T1 - Classification of liver diseases from CT images using BP-CMAC neural network

AU - Lee, Chien Cheng

AU - Chung, Pau-Choo

AU - Chen, Yieng Jair

PY - 2005/10/31

Y1 - 2005/10/31

N2 - 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.

AB - 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.

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M3 - Paper

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T2 - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA

Y2 - 28 May 2005 through 30 May 2005

ER -

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

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