Secondary Screening Algorithm for Breast Cancer Detection Using Matlab
Keywords:
Breast Cancer, Computer Aided Detection, CAD, Support Vector Machine, SVM, Gray-level Co-occurance Matrix, GLCM, k-nearest Neighbor, KNN.Abstract
Physicians and radiologists utilize computer-aided detection (CAD) systems to detect breast cancer. In this study, through the use of CAD we are going to detect abnormal tumors in X-Ray images using statistical and histogram-based features along with 9 different SVM and KNN classifiers. DDSM from the University of South Florida is the source of the digital X-Ray images. The specificity, sensitivity, and accuracy is compared with previous similar studies.
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Henry NL, Shah PD, Haider I, Freer PE, Jagsi R, Sabel MS. Chapter 88: Cancer of the Breast. In: Niederhuber JE, Armitage JO, Doroshow JH, Kastan MB, Tepper JE, eds. Abeloff’s Clinical Oncology. 6th ed. Philadelphia, Pa: Elsevier; 2020.
Jagsi R, King TA, Lehman C, Morrow M, Harris JR, Burstein HJ. Chapter 79: Malignant Tumors of the Breast. In: DeVita VT, Lawrence TS, Lawrence TS, Rosenberg SA, eds. DeVita, Hellman, and Rosenberg’s Cancer: Principles and Practice of Oncology. 11th ed. Philadelphia, Pa: Lippincott Williams & Wilkins; 2019.
National Cancer Institute. Physician Data Query (PDQ). Breast Cancer Treatment – Patient Version. 2021. Accessed at https://www.cancer.gov/types/breast/patient/breast-treatment-pdq on June 24, 2021.
Patlak M, Nass SJ, Henderson IC, et al., “Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer: A Non-Technical Summary.” Institute of Medicine (US) and National Research Council (US) Committee on the Early Detection of Breast Cancer; Washington (DC): National Academies Press (US); 2001
Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura, and Rie Kawakami, "Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine," International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 4, pp. 23-28, 2013.
K. B. Soulami, M. N. Saidi and A. Tamtaoui, "A CAD system for the detection and classification of abnormalities in dense mammograms using electromagnetism-like optimization algorithm," 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, Morocco, 2017, pp. 1-8, doi: 10.1109/ATSIP.2017.8075533.
Mellisa Pratiwi, Alexander, Jeklin Harefa, Sakka Nanda, “Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network”, International Conference on Computer Science and Computational Intelligence (ICCSCI 2015), Procedia Computer Science 59 (2015), 83 – 91.
Arden Sagiterry Setiawan, Elysia, Julian Wesley, Yudy Purnama, "Mammogram Classification using Law’s Texture Energy Measure and Neural Networks," International Conference on Computer Science and Computational Intelligence (ICCSCI 2015), Procedia Computer Science 59 (2015) 92 – 97.
A. K. Q. K. Ghada Saad, "ANN and Adaboost application for automatic detection of microcalcifications in breast cancer," The Egyptian Journal of Radiology and Nuclear Medicine, vol. 47, pp. 1803-1814, 2016.
L. L. Pavel Kra’1, "LBP Features for Breast Cancer Detection," in International Conference on Image Processing, Arizona, 2016.
Yahia Osman, Umar Alqasemi, “Breast Cancer Computer-Aided Detection System based on Simple Statistical Features and SVM Classification,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 1, Jan. 2020.
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