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Tumor Cell Image Recognition Based on PCA and Two-Level SOFM

  • Lan Gan
  • Chunmei He
  • Lijuan Xie
  • Wenya Lv
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)

Abstract

In this paper, a method based on PCA and two-level SOFM neural network is proposed for tumor recognition. The method combines PCA with a two-level SOFM neural network in which PCA is used to reduce the dimensionality of the input tumor image sample and the two-level SOFM neural network is used to extract characters and classifying. This method compromises linear dimensionality reduction, character extraction and classification. The training learning of the tumor image samples in the clinical pathological diagnosis can get the parameter of the two-level SOFM neural network. The experiment shows that the proposed method has better classifying accuracy and the classifying time is letter than the other methods such as PCA, LLE, PCA+LDA, SVM and two-level SOFM.

Keywords

tumor recognition feature extraction PCA SOFM 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangChina

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