Fractal Dimension Estimation for Developing Pathological Brain Detection System Based on Minkowski-Bouligand Method

Yu Dong Zhang, Xian Qing Chen, Tian Ming Zhan, Zhu Qing Jiao, Yi Sun, Zhi Min Chen, Yu Yao, Lan Ting Fang, Yi Ding Lv, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

Abstract

It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of hidden neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.

Original languageEnglish
Article number7572925
Pages (from-to)5937-5947
Number of pages11
JournalIEEE Access
Volume4
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • K-fold cross validation
  • Minkowski Bouligand dimension
  • artificial bee colony
  • genetic algorithm
  • logistic map
  • number of hidden neuron

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