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

^*此作品的通讯作者

Chengdu University
Sichuan University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

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.

源语言	英语
主期刊名	Advances in Neural Networks - ISNN 2006
主期刊副标题	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
出版商	Springer Verlag
页	754-759
页数	6
ISBN（印刷版）	3540344829, 9783540344827
DOI	https://doi.org/10.1007/11760191_111
出版状态	已出版 - 2006
已对外发布	是
活动	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, 中国期限: 28 5月 2006 → 1 6月 2006

丛书

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	3973 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
国家/地区	中国
市	Chengdu
时期	28/05/06 → 1/06/06

访问文件

10.1007/11760191_111

其它文件与链接

链接到 Scopus 的出版物

引用此

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

Lin, Jiangli ; Shen, Xianhua ; Wang, Tianfu 等. / 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. 页码 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. 在 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), 卷 3973 LNCS, Springer Verlag, 页码 754-759, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, 中国, 28/05/06. https://doi.org/10.1007/11760191_111

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

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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.

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DO - 10.1007/11760191_111

M3 - 会议稿件

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SN - 9783540344827

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

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Lin J, Shen X, Wang T, Li D, Luo Y, Wang L. Recognition of fatty liver using hybrid neural network. 在 Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. 页码 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

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