Recognition of fatty liver using hybrid neural network

Authors:
Jiangli Lin;XianHua Shen;Tianfu Wang;Deyu Li;Yan Luo;Ling Wang
Affiliations:
Department of Biomedical Engineering, Sichuan University, Chengdu, China;Department of Biomedical Engineering, Sichuan University, Chengdu, China;Department of Biomedical Engineering, Sichuan University, Chengdu, China;Department of Biomedical Engineering, Sichuan University, Chengdu, China;Ultrasound Departments, the First Huaxi Hospital, Sichuan University, Chengdu, China;Department of Biomedical Engineering, Sichuan University, Chengdu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 0

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

IEEE Transactions on Information Technology in Biomedicine
Reduction of breast biopsies with a modified self-organizing map

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A hybrid neural network based on self-organizing map (SOM) and multilayer perception(MLP) artificial neural network(ANN) is proposed for recognition of fatty liver from B-scan ultrasonic images. Firstly, four texture features including angular second moment, contrast, entropy and inverse differential moment were extracted from gray-level co-occurrence matrices of B-scan ultrasound liver images. They were mapped by a SOM for feature reduction, and then combined with other two features, named approximate entropy and mean intensity ratio. All features were imposed to a MLP for recognition. In the experiment, 130 B-scan liver images were divided into two groups: 104 in training group and 26 in validation group. Both the normal and fatty livers were recognized correctly. This study showed that the hybrid neural network could be used for fatty liver recognition with good performances.