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Title: A novel feature reduction framework for digital mammogram image classification
Contributor(s): Alharbi, Hajar Mohammedsaleh H (author); Falzon, Gregory  (author)orcid ; Kwan, Paul H  (author)
Publication Date: 2015
DOI: 10.1109/ACPR.2015.7486498
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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.
Publication Type: Conference Publication
Conference Name: ACPR 2015: 3rd Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia, 3rd - 6th November, 2015
Conference Details: ACPR 2015: 3rd Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia, 3rd - 6th November, 2015
Source of Publication: Proceedings of the Third IAPR Asian Conference on Pattern Recognition (ACPR 2015), p. 221-225
Publisher: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: New York, United States of America
Field of Research (FOR): 080106 Image Processing
111202 Cancer Diagnosis
080109 Pattern Recognition and Data Mining
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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