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

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

Brijesh Verma (Central Queensland University, Australia) and Rinku Panchal (Central Queensland University, Australia)
DOI: 10.4018/978-1-59904-941-0.ch056
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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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