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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lin, J., Shen, X., Wang, T., Li, D., Luo, Y., Wang, L. (2006). Recognition of Fatty Liver Using Hybrid Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_111
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DOI: https://doi.org/10.1007/11760191_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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