Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

Shui Hua Wang, Khan Muhammad, Preetha Phillips, Zhengchao Dong, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalJournal of Ambient Intelligence and Humanized Computing
DOIs
Publication statusAccepted/In press - 27 Nov 2017
Externally publishedYes

Keywords

  • Box-counting dimension
  • Breast thermography
  • Ductal carcinoma in situ
  • Extreme learning machine
  • Fractal dimension

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