2011 International Conference on Emerging Trends in Electrical and Computer Technology 2011
DOI: 10.1109/icetect.2011.5760205
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Classification of malignant and benign microcalcification using SVM classifier

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Cited by 40 publications
(12 citation statements)
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“…Dheeba et al [14] used a Support Vector Machine (SVM) for the classification of micro-calcification clusters. They computed a feature representation for each Region of Interests (RoI), and then used that feature as input for the SVM.…”
Section: Introductionmentioning
confidence: 99%
“…Dheeba et al [14] used a Support Vector Machine (SVM) for the classification of micro-calcification clusters. They computed a feature representation for each Region of Interests (RoI), and then used that feature as input for the SVM.…”
Section: Introductionmentioning
confidence: 99%
“…SVM has an extra advantage of automatic model selection in the sense that both the optimal number and locations of the basic functions is automatically obtained during training. The performance of SVM largely depends on the kernel (Dheeba and Selvi, 2011).…”
Section: Support Vector Machinementioning
confidence: 99%
“…Support vector machines are employed for classification. Dheeba and Selvi (2011) proposed an algorithm for the classification of microcalcification in digital mammograms using Support Vector machine. To improve the classification rate Law's texture energy measures are taken from the image Region of Interest (ROI).…”
Section: Introductionmentioning
confidence: 99%
“…A set of Histopathological images has been classified using Scale Invariant Feature Transform (SIFT) and Discrete Cosine Transform (DCT) features with an SVM for classification by Mhala et al [3]. Law's Texture features have been utilized for Mammogram (322 images) image classification and 86.10% accuracy obtained by Dheeba et al [4]. Taheri et al [5] utilized intensity information, Auto Correlation Matrix and Energy values for breast-image classification and obtained 96.80% precision and 92.50% recall with 600 Mammogram images.…”
Section: Introductionmentioning
confidence: 99%