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

Authors:
M. Muthu Rama Krishnan;Shuvo Banerjee;Chinmay Chakraborty;Chandan Chakraborty;Ajoy K. Ray
Affiliations:
School of Medical Science and Technology, I.I.T Kharagpur, India;Department of Electrical Engineering, I.I.T. Kharagpur, India;G S Sanyal School of Telecommunication, I.I.T. Kharagpur, India;School of Medical Science and Technology, I.I.T Kharagpur, India;School of Medical Science and Technology, I.I.T Kharagpur, India and Electronics and Electrical Communication Engineering, I.I.T. Kharagpur, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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.