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Title: A method for the classification of mammograms using a statistical-based feature extraction

Authors: Nebi Gedik

Addresses: Institute of Health Sciences, University of Health Sciences, Istanbul, Turkey

Abstract: This paper represents a classification system for mammograms using wave atom transforms and feature selection process with t-test statistics. Mammogram images are transformed to the wave atom coefficients using wave atom transform. Next, a matrix is constructed from the coefficients. The matrix is used as the feature matrix in order to classify mammograms. To achieve the maximum classification accuracy rate, t-test statistics with a dynamic thresholding is additionally carried out. As a classifier, support vector machine is employed in the classification phase. According to the experimental results, the method proposed in this paper provides a successful contribution for the classification of mammographic images.

Keywords: mammogram; classification; feature extraction; feature selection; thresholding; t-test statistics; wave atom transform; WAT; SVM; normal-abnormal classification; benign-malignant classification.

DOI: 10.1504/IJBET.2022.120865

International Journal of Biomedical Engineering and Technology, 2022 Vol.38 No.1, pp.99 - 108

Received: 17 Sep 2018
Accepted: 26 Oct 2018

Published online: 15 Feb 2022 *

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