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Computer-aided detection system for breast cancer based on GMM and SVM
Joint Authors
Hifni, H. A.
al-Sukkari, N.
Asad, A. H.
Arafah, A. A.
Source
Arab Journal of Nuclear Sciences and Applications
Issue
Vol. 52, Issue 2 (30 Apr. 2019), pp.142-150, 9 p.
Publisher
The Egyptian Society of Nuclear Science and Applications
Publication Date
2019-04-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Region-of-interest (ROI) segmentation is an important critical step and challenging task in the evolution of computer-aided detection (CAD) system for breast cancer.
The discovery of breast cancer in early stages can save many women lives.However, most of the early detection systems are costly in terms of complexity,price and processing time; that make it unsuited for developing countries.The digital mammography is proven to be one of the most important diagnostic techniques for breast cancer tumors.
Therefore, this work proposes a CADsystem for breast cancer detectionfrom digital mammography based on GaussianMixture Model(GMM) followed bySupport Vector Machine (SVM).The best contribution of our proposed system is the usage of GMM for the first time in the literature for mammogram images segmentation into ROI areas.Besides, the discrimination betweenthe three classes of tissues as normal,benign ormalignant, isused without previous knowledge of mammogram images’type.
Moreover, the proposed system is fully automated in all of its stages with reduced computation compared with recent used methods.
Hence, it offers a suitable early detection system to our country regarding moneywise, timewise, and reduced complexity.A non-linear multi-class SVMis used for classifying the ROI into three classes: normal, benignor malignant tissue.The experiments show overall average classification accuracy of 90% for detecting normal, malignant or benignonrandomly chosen90 cases from the benchmark mini-MIAS dataset.
On the other hand, the proposed method achieves 92.5% accuracy when classifying the benign from malignant cases.
The confusion matrix was used to assess the proposed CAD system overall performance as it is considered to be a good evaluation metric method.
American Psychological Association (APA)
al-Sukkari, N.& Arafah, A. A.& Asad, A. H.& Hifni, H. A.. 2019. Computer-aided detection system for breast cancer based on GMM and SVM. Arab Journal of Nuclear Sciences and Applications،Vol. 52, no. 2, pp.142-150.
https://search.emarefa.net/detail/BIM-892508
Modern Language Association (MLA)
al-Sukkari, N.…[et al.]. Computer-aided detection system for breast cancer based on GMM and SVM. Arab Journal of Nuclear Sciences and Applications Vol. 52, no. 2 (Apr. 2019), pp.142-150.
https://search.emarefa.net/detail/BIM-892508
American Medical Association (AMA)
al-Sukkari, N.& Arafah, A. A.& Asad, A. H.& Hifni, H. A.. Computer-aided detection system for breast cancer based on GMM and SVM. Arab Journal of Nuclear Sciences and Applications. 2019. Vol. 52, no. 2, pp.142-150.
https://search.emarefa.net/detail/BIM-892508
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 149-150
Record ID
BIM-892508