Recognition of fatty liver using hybrid neural network

Jiangli Lin; Xianhua Shen; Tianfu Wang; Deyu Li; Yan Luo; Ling Wang

doi:10.1007/11760191_111

Recognition of fatty liver using hybrid neural network

Jiangli Lin^*
, Xianhua Shen
, Tianfu Wang
, Deyu Li
, Yan Luo
, Ling Wang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
Publisher	Springer Verlag
Pages	754-759
Number of pages	6
ISBN (Print)	3540344829, 9783540344827
DOIs	https://doi.org/10.1007/11760191_111
State	Published - 2006
Externally published	Yes
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3973 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

Access to Document

10.1007/11760191_111

Cite this

Lin, J., Shen, X., Wang, T., Li, D., Luo, Y., & Wang, L. (2006). Recognition of fatty liver using hybrid neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III (pp. 754-759). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS). Springer Verlag. https://doi.org/10.1007/11760191_111

Lin, Jiangli ; Shen, Xianhua ; Wang, Tianfu et al. / Recognition of fatty liver using hybrid neural network. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. pp. 754-759 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c3ee27992b59472abef0eba37ae97886,

title = "Recognition of fatty liver using hybrid neural network",

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.",

author = "Jiangli Lin and Xianhua Shen and Tianfu Wang and Deyu Li and Yan Luo and Ling Wang",

year = "2006",

doi = "10.1007/11760191\_111",

language = "英语",

isbn = "3540344829",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "754--759",

booktitle = "Advances in Neural Networks - ISNN 2006",

address = "德国",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Lin, J, Shen, X, Wang, T, Li, D, Luo, Y & Wang, L 2006, Recognition of fatty liver using hybrid neural network. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3973 LNCS, Springer Verlag, pp. 754-759, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760191_111

Recognition of fatty liver using hybrid neural network. / Lin, Jiangli; Shen, Xianhua; Wang, Tianfu et al.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. p. 754-759 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Recognition of fatty liver using hybrid neural network

AU - Lin, Jiangli

AU - Shen, Xianhua

AU - Wang, Tianfu

AU - Li, Deyu

AU - Luo, Yan

AU - Wang, Ling

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/33745922057

U2 - 10.1007/11760191_111

DO - 10.1007/11760191_111

M3 - 会议稿件

AN - SCOPUS:33745922057

SN - 3540344829

SN - 9783540344827

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 754

EP - 759

BT - Advances in Neural Networks - ISNN 2006

PB - Springer Verlag

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 28 May 2006 through 1 June 2006

ER -

Lin J, Shen X, Wang T, Li D, Luo Y, Wang L. Recognition of fatty liver using hybrid neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. p. 754-759. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760191_111

Recognition of fatty liver using hybrid neural network

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this