Conceptual data sampling for breast cancer histology image classification

Eman Rezk, Zainab Awan, Fahad Islam, Ali Jaoua, Somaya Al Maadeed, Nan Zhang, Gautam Das, Nasir Rajpoot

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure.

Original languageEnglish (US)
Pages (from-to)59-67
Number of pages9
JournalComputers in Biology and Medicine
StatePublished - Oct 1 2017

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics


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