Breast tumor computer-aided diagnosis using self-validating cerebellar model neural networks

Jian Sheng Guan, Lo Yi Lin, Guo Li Ji, Chih Min Lin, Tien Loc Le, I. Rudas

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Breast cancer is becoming a leading cause of death among women in the world. However, it is confirmed that early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This study proposes a self-validation cerebellar model articulation controller (SVCMAC) neural network which can yield high accuracy of predication and low false-negative rate for breast cancer diagnosis. With its self-validation unit, the SVCMAC neural network has higher classification accuracy than the conventional CMAC neural network. The parameters of the receptive-field basis function and the weights are all updated first by training data, and the most suitable parameters are then chosen through the self-validation algorithm to retrain the neural network for better performance. Experimental results provide evidence that the SVCMAC neural network has a higher classification accuracy when compared with the BP neural network, LVQ neural network and CMAC neural network.

Original languageEnglish
Pages (from-to)39-52
Number of pages14
JournalActa Polytechnica Hungarica
Volume13
Issue number4
DOIs
Publication statusPublished - 2016

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Computer aided diagnosis
Tumors
Neural networks
Controllers

Keywords

  • Breast cancer diagnosis
  • Cerebellar model articulation controller
  • Self-validation

ASJC Scopus subject areas

  • Engineering(all)
  • General

Cite this

Breast tumor computer-aided diagnosis using self-validating cerebellar model neural networks. / Guan, Jian Sheng; Lin, Lo Yi; Ji, Guo Li; Lin, Chih Min; Le, Tien Loc; Rudas, I.

In: Acta Polytechnica Hungarica, Vol. 13, No. 4, 2016, p. 39-52.

Research output: Contribution to journalArticle

Guan, Jian Sheng ; Lin, Lo Yi ; Ji, Guo Li ; Lin, Chih Min ; Le, Tien Loc ; Rudas, I. / Breast tumor computer-aided diagnosis using self-validating cerebellar model neural networks. In: Acta Polytechnica Hungarica. 2016 ; Vol. 13, No. 4. pp. 39-52.
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