Entropy-based breast cancer detection in digital mammograms using world cup optimization algorithm

Navid Razmjooy, Vania V. Estrela, Hermes Jose Loschi

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)


Breast cancer is one of the deadliest cancers for women. Early detection of skin cancer gives a high chance for the women to escape from the malady and obtain a cure at the initial stages. In other words, early detection of breast cancer has a direct relation by the women's quality of life. In this case, mammography images are important. Indeed, the main test used for screening and early diagnosis of breast cancer is mammography. In recent years, computer-aided cancer detection has been turned into an active field of research and showed a promising future. In this study, a new optimization algorithm based on thresholding is introduced. A WCO algorithm is employed as the optimization algorithm. WCO is a new meta-heuristic approach which is inspired by the FIFA world cup challenge. The presented method utilizes random samples as candidate solutions from the search space inside the image histogram with considering to the objective function that is utilized by the Kapur's method.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalInternational Journal of Swarm Intelligence Research
Issue number3
Publication statusPublished - 1 Jul 2020
Externally publishedYes


  • Breast Cancer
  • Entropy
  • Image Processing
  • Kapur Thresholding
  • Mammography
  • Segmentation
  • World Cup Optimization


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