Breast cancer computer-aided detection system based on simple statistical features and SVM classification

被引：0

作者：

Osman Y. ^{[1
]}

Alqasemi U. ^{[1
]}

机构：

[1] Biomedical Engineering Program, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah

来源：

| 1600年 / Science and Information Organization卷 / 11期

关键词：

Breast cancer; Clusters; Computer-aided detection systems; Features extraction; KNN; Mammogram; MIAS; ROI; SVM;

D O I：

10.14569/ijacsa.2020.0110153

中图分类号：

学科分类号：

摘要：

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. © 2013 The Science and Information (SAI) Organization.

引用

页码：430 / 433

页数：3

共 14 条

[1]

Control D.O.C.P.A., Centers for Disease Control and Prevention, 11 Seitember 2018, (2019)

[2]

Organization W.H., World Health Organization, 12 Seitember 2018, (2019)

[3]

Alqasemi P.U.S., Interviewee, Assistant Professor of Biomedical Engineering, (2019)

[4]

Kohei Arai I.N.A.H.O.R.K., Imirovement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine, International Journal of Advanced Research in Artificial Intelligence, 2, 4, pp. 23-28, (2013)

[5]

Khaoula Belhaj Soulami M.N.S.A.T., A CAD system for the detection and classification of abnormalities in dense mammograms using Electromagnetism-like oitimization algorithm, 3rd International Conference on Advanced Technologies for Signal and Image Processing, (2017)

[6]

Mellisa Pratiwi S.N., Mammograms Classification using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network, International Conference on Comiuter Science and Comiutational Intelligence, (2015)

[7]

Arden Sagiterry Setiawan J.W.A.Y.P., Mammogram Classification using Law's Texture Energy Measure and Neural Networks, International Conference on Comiuter Science and Comiutational Intelligence, (2015)

[8]

Ghada Saad A.K.Q.K., ANN and Adaboost application for automatic detection of microcalcifications in breast cancer, The Egyitian Journal of Radiology and Nuclear Medicine, 47, pp. 1803-1814, (2016)

[9]

Pavel Kra l L.L., LBP Features for Breast Cancer Detection, International Conference on Image Processing, (2016)

[10]

Al-Antari M.A.S., Comiuter-Aided Breast Cancer Detection and Diagnosis from Digital Mammograms, Faculty of Engineering, (2015)

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