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Conceptual data sampling for breast cancer histology image classification

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Rezk, Eman, Awan, Zainab, Islam, Fahad, Jaoua, Ali, Al Maadeed, Somaya, Zhang, Nan, Das, Gautam and Rajpoot, Nasir M. (Nasir Mahmood) (2017) Conceptual data sampling for breast cancer histology image classification. Computers in Biology and Medicine, 89 . pp. 59-67. doi:10.1016/j.compbiomed.2017.07.018

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Official URL: http://dx.doi.org/10.1016/j.compbiomed.2017.07.018

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Abstract

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.

Item Type: Journal Article
Subjects: R Medicine > RG Gynecology and obstetrics
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Breast -- Imaging., Breast -- Radiography., Breast -- Cancer -- Diagnosis., Breast -- Histopathology.
Journal or Publication Title: Computers in Biology and Medicine
Publisher: Pergamon
ISSN: 0010-4825
Official Date: 1 October 2017
Dates:
DateEvent
1 October 2017Published
29 July 2017Available
28 July 2017Accepted
Volume: 89
Page Range: pp. 59-67
DOI: 10.1016/j.compbiomed.2017.07.018
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
07- 794-1-145Qatar National Research Fundhttp://dx.doi.org/10.13039/100008982

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