Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer

Article


Verma, Brijesh, McLeod, Peter and Klevansky, Alan. 2010. "Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer." Expert Systems with Applications. 37 (4), pp. 3344-3351. https://doi.org/10.1016/j.eswa.2009.10.016
Article Title

Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer

ERA Journal ID17852
Article CategoryArticle
AuthorsVerma, Brijesh (Author), McLeod, Peter (Author) and Klevansky, Alan (Author)
Journal TitleExpert Systems with Applications
Journal Citation37 (4), pp. 3344-3351
Number of Pages8
Year2010
PublisherElsevier
Place of PublicationNetherlands
ISSN0957-4174
1873-6793
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2009.10.016
Web Address (URL)http://www.sciencedirect.com/science/article/pii/S0957417409008823#
Abstract

The classification of benign and malignant patterns in digital mammograms is one of most important and significant processes during the diagnosis of breast cancer as it helps detecting the disease at its early stage which saves many lives. Breast abnormalities are often embedded in and camouflaged by various breast tissue structures. It is a very challenging and difficult task for radiologists to correctly classify suspicious areas (benign and malignant patterns) in digital mammograms. In the early stage, the visual clues are subtle and varied in appearance, making diagnosis difficult; challenging even for specialists. Therefore, an intelligent classifier is required which can help radiologists in classifying suspicious areas and diagnosing breast cancer. This paper investigates a novel soft clustered based direct learning classifier which creates soft clusters within a class and learns using direct calculation of weights. The feature space for suspicious areas in digital mammograms from same class patterns can have multiple clusters and the proposed classifier uses this fact and introduces a novel idea to create soft clusters for each available class and applies them to form sub-classes within benign and malignant classes. A novel learning process based on direct learning is introduced. The experiments using the proposed classifier have been conducted on a benchmark database. The results have been analysed using ANOVA test which showed that the results are statistically significant.

Keywordsintelligent classifiers; neural networks; digital mammography
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
320602. Medical biotechnology diagnostics (incl. biosensors)
320222. Radiology and organ imaging
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Byline AffiliationsCentral Queensland University
Department of Health, Queensland
Institution of OriginUniversity of Southern Queensland
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A novel soft cluster neural network for the classification of suspicious areas in digital mammograms
Verma, Brijesh, McLeod, Peter and Klevansky, Alan. 2009. "A novel soft cluster neural network for the classification of suspicious areas in digital mammograms." Pattern Recognition. 42 (9), pp. 1845-1852. https://doi.org/10.1016/j.patcog.2009.02.009