Conceptual data sampling for breast cancer histology image classification
Author | Rezk, Eman |
Author | Awan, Zainab |
Author | Islam, Fahad |
Author | Jaoua, Ali |
Author | Al Maadeed, Somaya |
Author | Zhang, Nan |
Author | Das, Gautam |
Author | Rajpoot, Nasir |
Available date | 2020-11-12T07:55:57Z |
Publication Date | 2017 |
Publication Name | Computers in Biology and Medicine |
Resource | Scopus |
ISSN | 104825 |
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. 1 2017 Elsevier Ltd |
Sponsor | This 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. |
Language | en |
Publisher | Elsevier Ltd |
Subject | Breast cancer classification Data sampling Formal concept analysis Histopathology Image segmentation |
Type | Article |
Pagination | 59-67 |
Volume Number | 89 |
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