Pauline McLeod

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This paper presents a technique which explores the fusion of clustering and a least square method for the classification of suspicious areas within digital mammograms into benign and malignant classes. It incorporates a clustering algorithm such as k-means in conjunction with a gram-schmidt based least square method. The main focus of the research presented(More)
The fusion of clustering and least square based method for the classification of suspicious areas into benign and malignant classes in digital mammograms was investigated in our previous paper which showed some promising results. This paper extends the investigation by combining a self organising map (SOM) based clustering with modified Gram-Schmidt (MGS)(More)
This paper investigates a soft cluster based approach for determining the impact of soft clustering on the training of a neural network classifier for the classification of suspicious areas in digital mammograms. An approach is proposed that first creates soft clusters for each available class and then uses soft clusters to form subclasses within benign and(More)
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