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Recognition of Fatty Liver Using Hybrid Neural Network

  • Jiangli Lin
  • XianHua Shen
  • Tianfu Wang
  • Deyu Li
  • Yan Luo
  • Ling Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Fatty Liver Back Propagation Neural Network Validation Group Ultrasonic Image Approximate Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiangli Lin
    • 1
  • XianHua Shen
    • 1
  • Tianfu Wang
    • 1
  • Deyu Li
    • 1
  • Yan Luo
    • 2
  • Ling Wang
    • 1
  1. 1.Department of Biomedical EngineeringSichuan UniversityChengduChina
  2. 2.Ultrasound Departments, the First Huaxi HospitalSichuan UniversityChengduChina

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