Classification of Breast Cancer Image Using Data Mining Techniques

Mohamed Alhag Alobed; Ali Ahmed; Ashraf Osman Ibrahim

doi:doi:10.11648/j.ajdmkd.20210602.13

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Classification of Breast Cancer Image Using Data Mining Techniques

Mohamed Alhag Alobed, Ali Ahmed, Ashraf Osman Ibrahim

Published in American Journal of Data Mining and Knowledge Discovery (Volume 6, Issue 2)

Received: 12 October 2021 Accepted: 1 November 2021 Published: 25 November 2021

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Abstract

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.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 6, Issue 2)
DOI	10.11648/j.ajdmkd.20210602.13
Page(s)	31-35
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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[5]	Tang, J., Rangayyan, R. M., Xu, J., El Naqa, I., & Yang, Y. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. Information Technology in Biomedicine, IEEE Transactions on, 13 (2), 23.
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[7]	Affar, M. A., Hybrid Texture based Classification of Brea Mammograms using Ad boost Classifier. International Journal of Advanced Computer Science and Applications, 2017. 8 (5).
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[18]	S. Usha, S. Arumugam (2015). “Calcification Classification in Mammograms Using Decision Trees” International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol: 9, No: 9, 201.

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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

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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

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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

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@article{10.11648/j.ajdmkd.20210602.13,
  author = {Mohamed Alhag Alobed and Ali Ahmed and Ashraf Osman Ibrahim},
  title = {Classification of Breast Cancer Image Using Data Mining Techniques},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {6},
  number = {2},
  pages = {31-35},
  doi = {10.11648/j.ajdmkd.20210602.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20210602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20210602.13},
  abstract = {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.},
 year = {2021}
}

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TY  - JOUR
T1  - Classification of Breast Cancer Image Using Data Mining Techniques
AU  - Mohamed Alhag Alobed
AU  - Ali Ahmed
AU  - Ashraf Osman Ibrahim
Y1  - 2021/11/25
PY  - 2021
N1  - https://doi.org/10.11648/j.ajdmkd.20210602.13
DO  - 10.11648/j.ajdmkd.20210602.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 31
EP  - 35
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20210602.13
AB  - 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.
VL  - 6
IS  - 2
ER  -

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Author Information

Mohamed Alhag Alobed

Tumor Therapy and Cancer Research Center, Shendi University, Shendi, Sudan
Ali Ahmed

Faculty of Computer Science and Information Technology, Karary University, Omdurman, Sudan
Ashraf Osman Ibrahim

Faculty of Computer Science and Information Technology, Alzaiem Alazhari University, Khartoum, Sudan

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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

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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

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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

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@article{10.11648/j.ajdmkd.20210602.13,
  author = {Mohamed Alhag Alobed and Ali Ahmed and Ashraf Osman Ibrahim},
  title = {Classification of Breast Cancer Image Using Data Mining Techniques},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {6},
  number = {2},
  pages = {31-35},
  doi = {10.11648/j.ajdmkd.20210602.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20210602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20210602.13},
  abstract = {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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Classification of Breast Cancer Image Using Data Mining Techniques
AU  - Mohamed Alhag Alobed
AU  - Ali Ahmed
AU  - Ashraf Osman Ibrahim
Y1  - 2021/11/25
PY  - 2021
N1  - https://doi.org/10.11648/j.ajdmkd.20210602.13
DO  - 10.11648/j.ajdmkd.20210602.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 31
EP  - 35
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20210602.13
AB  - 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.
VL  - 6
IS  - 2
ER  -

Copy | Download