Statistical analysis of mammographic features and its classification using support vector machine

TY - JOUR

T1 - Statistical analysis of mammographic features and its classification using support vector machine

AU - Mookiah, Muthu Rama Krishnan

AU - Banerjee, S.

AU - Chakraborty, Chinmay

AU - Chakraborty, Chandan

AU - Ray, A.K.

PY - 2010/1

Y1 - 2010/1

N2 - This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.

AB - This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.

KW - Mammogram based data

KW - Statistical analysis

KW - Support vector machine

KW - Kernel function

KW - Diagnostic measures

UR - https://www.scopus.com/pages/publications/70349566412

U2 - 10.1016/j.eswa.2009.05.045

DO - 10.1016/j.eswa.2009.05.045

M3 - Article

SN - 0957-4174

VL - 37

SP - 470

EP - 478

JO - Expert Systems with Applications

JF - Expert Systems with Applications

IS - 1

ER -