Evolutionary Fuzzy Extreme Learning Machine for Mammographic Risk Analysis

Yanpeng Qu, Changjing Shang*, Wei Wu, Qiang Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Mammographic risk analysis is an important and challenging issue in modern medical science; research and development in this area has recently attracted much attention. Many efforts have been devoted to achieving a higher accuracy in such analysis. This paper presents a novel approach for automated analysis of mammographic risk, in support of human consultant estimation of such risk. The underlying approach is general, it combines evolutionary computation with extreme learning machine to efficiently train effective fuzzy systems. The proposed approach is experimentally compared to a number of state-of-the-art learning classifiers that can also be adopted to analyze mammographic risk. The significance of this work is highlighted by its improved performance over the alternative approaches, measured using criteria such as classification accuracy and confusion matrices. The results demonstrate that for the problem of mammographic risk analysis, evolutionary fuzzy extreme learning machine entails such performance both at the overall image level and at the level of individual risk types.

Original languageEnglish
Article number2552658
Pages (from-to)282-291
Number of pages10
JournalInternational Journal of Fuzzy Systems
Issue number4
Publication statusPublished - Dec 2011


  • Mammographic Risk Analysis
  • Differential Evolution
  • TSK Fuzzy Systems
  • Extreme Learning Machine


Dive into the research topics of 'Evolutionary Fuzzy Extreme Learning Machine for Mammographic Risk Analysis'. Together they form a unique fingerprint.

Cite this