Impact of multiple clusters on neural classification of ROIs in digital mammograms

This paper evaluates the impact of multiple clusters on neural classification of regions of interest (ROIs) in digital mammograms. The training and test sets for neural networks usually contain inputs extracted from ROIs and relevant class such as benign and malignant. However, the patterns such as regions of interest in digital mammograms do not have just one cluster per class instead they have many clusters within benign and malignant classes. Therefore, neural network training may benefit in terms of accuracy and efficiency by creating and analyzing a number of clusters within a class. A novel multiple clusters based neural classification approach is presented. In this approach, input data is clustered into a number of clusters per class and a neural classifier is trained with clustered data which contain multiple clusters per class. The experiments on a benchmark database of digital mammograms are conducted. The results show that the multiple clusters per class have significant impact on neural classification and overall they achieve better accuracy than single cluster per class based classification of ROIs in digital mammograms.