Title :
A novel feature reduction framework for digital mammogram image classification
Author :
Hajar Alharbi;Gregory Falzon;Paul Kwan
Author_Institution :
King Abdulaziz University, PO Box 80200, Jeddah 21589, Kingdom of Saudi Arabia
Abstract :
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.
Keywords :
"Feature extraction","Mammography","Genetic algorithms","Breast cancer","Entropy","Databases"
Conference_Titel :
Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on
Electronic_ISBN :
2327-0985
DOI :
10.1109/ACPR.2015.7486498