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

Author

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

Author_Institution

Dept. of Commun. Eng., Yuan Ze Univ., Chungli, Taiwan

fYear

2005

fDate

28-30 May 2005

Firstpage

118

Lastpage

121

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.

Keywords

backpropagation; cerebellar model arithmetic computers; computerised tomography; diseases; image classification; liver; medical image processing; BP-CMAC neural network; CT images; backpropagation; cavernous hemagioma; hepatoma; learning speedup; liver cyst; liver disease classification; liver disease diagnosis system; Computed tomography; Councils; Feature extraction; Humans; Image analysis; Image texture analysis; Liver diseases; Neural networks; Pathology; System testing; CMAC; back-propagation; cyst; hemagioma; hepatoma; liver;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and Their Applications, 2005 9th International Workshop on

Print_ISBN

0-7803-9185-3

Type

conf

DOI

10.1109/CNNA.2005.1543175

Filename

1543175