Open AccessBreast Tumor Classification Using SVM
Author(s) -
Joanne H. Al-Khalidy,
Raid R. Al-Ne’ma
Publication year - 2013
Publication title -
mağallaẗ tikrīt li-l-ʻulūm al-handasiyyaẗ/tikrit journal of engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 2312-7589
pISSN - 1813-162X
DOI - 10.25130/tjes.21.1.06
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , machine learning , computer science , breast tumor , breast cancer , medicine , cancer
Although there are several techniques that have been used to differentiate between benign andmalignant breast tumor lately, support vector machines (SVMs) have been distinguished as one ofthe common method of classification for many fields such as medical diagnostic, that it offersmany advantages with respect to previously proposed methods such as ANNs. One of them is thatSVM provide a higher accuracy, another advantage that SVM reduces the computational cost,and it is already showed good result in this work.In this paper, a Support Vector Machine for differentiation Breast tumor was presented torecognize malignant or benign in mammograms. This work used 569 cases and they wereclassified into two groups: malignant (+1) or benign (-1), then randomly selected some of thesesamples for training model while others were used for test. The ratios were 84.4.0% of acceptedfalse, 947142% of refused false. These results indicate how much this method is successful.