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Classification of Breast Cancer Image Using Data Mining Techniques

Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. This paper presented the classification method for mammogram Image using the decision tree techniques. Three measures were used to evaluate performance in terms of accuracy, sensitivity, and privacy. The aim of the study is to determine the best decision tree classifier for medical datasets classification. The study emphasizes five phases; starting with collecting images, pre-processing (image cropping of ROI), features extracting, classification and end with testing and evaluating. Experimental results show that Random Forest has a better performance than ID3, J48.

Mammograms, Breast Cancer, Decision Tree, Early Detection, Image Classification

APA Style

Mohamed Alhag Alobed, Ali Ahmed, Ashraf Osman Ibrahim. (2021). Classification of Breast Cancer Image Using Data Mining Techniques. American Journal of Data Mining and Knowledge Discovery, 6(2), 31-35. https://doi.org/10.11648/j.ajdmkd.20210602.13

ACS Style

Mohamed Alhag Alobed; Ali Ahmed; Ashraf Osman Ibrahim. Classification of Breast Cancer Image Using Data Mining Techniques. Am. J. Data Min. Knowl. Discov. 2021, 6(2), 31-35. doi: 10.11648/j.ajdmkd.20210602.13

AMA Style

Mohamed Alhag Alobed, Ali Ahmed, Ashraf Osman Ibrahim. Classification of Breast Cancer Image Using Data Mining Techniques. Am J Data Min Knowl Discov. 2021;6(2):31-35. doi: 10.11648/j.ajdmkd.20210602.13

Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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