Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms

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Author(s): Brijesh Verma (Central Queensland University, Australia)and Rinku Panchal (Central Queensland University, Australia)
Copyright: 2008
Pages: 21
Source title: Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Vijayan Sugumaran (Oakland University, Rochester, USA)
DOI: 10.4018/978-1-59904-941-0.ch056

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial neural network (ANN) applications in digital mammography are mainly focused on feature extraction, feature selection, and classification of micro-calcification patterns into ‘benign’ and ‘malignant’. An extensive review of neural network based techniques in digital mammography is presented. Recent developments such as auto-associators and evolutionary neural networks for feature extraction and selection are presented. Experimental results using ANN techniques on a benchmark database are described and analysed. Finally, a comparison of various neural network-based techniques is presented.

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Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms

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