Alharbi, Hajar Mohammedsaleh H

Falzon, Gregory

Kwan, Paul H

2015

The visual similarity between normal breast tissues and abnormal lesions in digital mammogram images makes computer-aided diagnosis of breast cancer using automatically detected features a highly error-prone task. Our contribution in this paper is a novel feature reduction framework for selecting the most discriminative features that achieves both efficiency and classification accuracy. Our approach applies five individual feature-ranking methods including Fisher score, minimum redundancy-maximum relevance, relief-f, sequential forward feature selection, and genetic algorithm for sorting the extracted features and selecting the features with highest ranking to setup a classifier. Our method achieves an accuracy of 94.27% and a sensitivity of 98.36% with a specificity of 99.27% on a set of 1,100 mammogram patches taken from image retrieval in medical applications database using a neural network classifier, which competes with state-of-the-art classification accuracy 93.11%. Furthermore, we demonstrate that only 49 out of the 119 extracted features are sufficient to achieve the reported accuracy of normal vs. abnormal classification.

Proceedings of the Third IAPR Asian Conference on Pattern Recognition (ACPR 2015), p. 221-225

9781479961009

https://hdl.handle.net/1959.11/19178

en

Institute of Electrical and Electronics Engineers (IEEE)

A novel feature reduction framework for digital mammogram image classification

Conference Publication

Publication