Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network

Authors:
Rinku Panchal;Brijesh Verma
Affiliations:
School of Information Technology, Faculty of Business & Informatics, Central Queensland University, Rockhampton, Australia;School of Information Technology, Faculty of Business & Informatics, Central Queensland University, Rockhampton, Australia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 2
Cited 1

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques

IEEE Transactions on Information Technology in Biomedicine

Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms

Pattern Recognition Letters

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Abstract

Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BIRADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.