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DOI: 10.14569/IJACSA.2020.0110153
PDF

Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification

Author 1: Yahia Osman
Author 2: Umar Alqasemi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.

  • Abstract and Keywords
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Abstract: Computer-Aided Detection (CADe) systems are becoming very helpful and useful in supporting physicians for early detection of breast cancer. In this paper, a CADe system that is able to detect abnormal clusters in mammographic images will be implemented using different classifiers and features. The CADe system will utilize a Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as classifiers. Adopting mammographic database from Mammographic Image Analysis Society (MIAS), for training and testing, the performance of the two types of classifiers are compared in terms of sensitivity, specificity, and accuracy. The obtained values for the previous parameters show the efficiency of the CADe system to be used as a secondary screening method in detecting abnormal clusters given the Region of Interest (ROI). The best classifier is found to be SVM showed 96% accuracy, 92% sensitivity and 100% specificity.

Keywords: Breast cancer; MIAS; features extraction; SVM; mammogram; clusters; computer-aided detection systems; KNN; ROI

Yahia Osman and Umar Alqasemi, “Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110153

@article{Osman2020,
title = {Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110153},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110153},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Yahia Osman and Umar Alqasemi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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