Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss

Shuihua Wang, Ming Yang, Jianwu Li, Xueyan Wu, Hainan Wang, Bin Liu, Zhengchao Dong, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

To detect the sensorineural hearing loss (SNHL) from healthy people accurately, we used magnetic resonance imaging (MRI) to obtain the imaging data, and then proposed a new computer-aided diagnosis (CAD) system, on the basis of texture analysis method. In the first, we extracted 12-element feature from each brain image via fractional Fourier entropy (FRFE). Afterwards, multilayer perceptron (MLP) was employed as the classifier, which was trained by a novel fitness-scaling adaptive genetic algorithm (FSAGA). The statistical analysis over 49 subjects showed the overall accuracy of our method yielded 95.51%. Experimental results performed better than four state-of-the-art weight optimization methods, and this CAD system give significantly better performance than manual interpretation.

Original languageEnglish
Pages (from-to)505-521
Number of pages17
JournalFundamenta Informaticae
Volume151
Issue number1-4
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Fractional Fourier entropy
  • Genetic algorithm
  • Power-rank fitness scaling
  • Sensorineural hearing loss
  • Texture analysis

Fingerprint

Dive into the research topics of 'Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss'. Together they form a unique fingerprint.

Cite this