Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer

The classification of benign and malignant patterns in digital mammograms is one of most important and significant processes during the diagnosis of breast cancer as it helps detecting the disease at its early stage which saves many lives. Breast abnormalities are often embedded in and camouflaged by various breast tissue structures. It is a very challenging and difficult task for radiologists to correctly classify suspicious areas (benign and malignant patterns) in digital mammograms. In the early stage, the visual clues are subtle and varied in appearance, making diagnosis difficult; challenging even for specialists. Therefore, an intelligent classifier is required which can help radiologists in classifying suspicious areas and diagnosing breast cancer. This paper investigates a novel soft clustered based direct learning classifier which creates soft clusters within a class and learns using direct calculation of weights. The feature space for suspicious areas in digital mammograms from same class patterns can have multiple clusters and the proposed classifier uses this fact and introduces a novel idea to create soft clusters for each available class and applies them to form sub-classes within benign and malignant classes. A novel learning process based on direct learning is introduced. The experiments using the proposed classifier have been conducted on a benchmark database. The results have been analysed using ANOVA test which showed that the results are statistically significant.