A novel soft cluster neural network for the classification of suspicious areas in digital mammograms

Authors:
Brijesh Verma;Peter McLeod;Alan Klevansky
Affiliations:
School of Computing Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia;School of Computing Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia;Radiology Department, Gold Coast Hospital, Gold Coast, QLD 4215, Australia
Venue:
Pattern Recognition
Year:
2009

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Abstract

This paper presents a novel soft cluster neural network technique for the classification of suspicious areas in digital mammograms. The technique introduces the concept of soft clusters within a neural network layer and combines them with least squares for optimising neural network weights. The idea of soft clusters is proposed in order to increase the generalisation ability of the neural network by providing a mechanism to more aptly depict the relationship between the input features and the subsequent classification as either a benign or malignant class. Soft clusters with least squares make the training process faster and avoid iterative processes which have many problems. The proposed neural network technique has been tested on the DDSM benchmark database. The results are analysed and discussed in this paper.