SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE and FRACTAL DIMENSION for IDENTIFYING MULTIPLE SCLEROSIS

Yu Dong Zhang, Yin Zhang, Preetha Phillips, Zhengchao Dong, Shuihua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski-Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy.

Original languageEnglish
Article number1740010
JournalFractals
Volume25
Issue number4
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Biogeography-Based Optimization
  • Box Counting
  • Canny Edge Detector
  • Fractal Dimension
  • Minkowski-Bouligand Dimension
  • Synthetic Minority Oversampling Technique
  • Three-Segment Representation

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