Author : S.Venkata Lakshmi 1
Date of Publication :7th March 2016
Abstract: Mammogram is one of the most commonly used radiology tool for the detection of breast cancer at the earlier stage, as it helps to reveal abnormalities such as masses, micro-calcification, asymmetries and architectural distortions. In this paper, we propose a technique for diagnosing breast cancer by using SVM classifier, which segregates on the basis of LBP features. SVM (Support Vector Machine)is a supervised learning models to analyze the given data and then recognize their pattern formats; extracted from the mammographic images by using LBP technique. Further Feature Extraction is performed by HOG technique. The HOG technique helps to specify the magnitude, phase and angle value of the scanned LBP regions. This proposed method using mammograms are solicited for a different set of asymmetric cases and normal cases in the mini-MIAS (Mammographic Image Analysis Society) database, from which their data were analyzed to obtain their effective estimations with the accuracy rate of 0.85 and above.
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