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AuthorRezk, Eman
AuthorAwan, Zainab
AuthorIslam, Fahad
AuthorJaoua, Ali
AuthorAl Maadeed, Somaya
AuthorZhang, Nan
AuthorDas, Gautam
AuthorRajpoot, Nasir
Available date2020-11-12T07:55:57Z
Publication Date2017
Publication NameComputers in Biology and Medicine
ResourceScopus
ISSN104825
URIhttp://dx.doi.org/10.1016/j.compbiomed.2017.07.018
URIhttp://hdl.handle.net/10576/16972
AbstractData 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. 1 2017 Elsevier Ltd
SponsorThis contribution was made possible by NPRP grant #07- 794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherElsevier Ltd
SubjectBreast cancer classification
Data sampling
Formal concept analysis
Histopathology
Image segmentation
TitleConceptual data sampling for breast cancer histology image classification
TypeArticle
Pagination59-67
Volume Number89
dc.accessType Abstract Only


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