Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies

Grading of breast cancer malignancy is a key step in its diagnosis, which in turn helps to determine its prognosis and a course of treatment. In this paper, we consider the application of pattern recognition and image processing techniques to perform computer-assisted automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a classification of the malignancy of the slide, a feature set is first determined from imagery of the slides. In this paper we investigated the nature of a wide set of features extracted from biopsy images to determine their discriminatory power and cross-correlation. Feature vector reduction is studied using a correlation map of the features, determining discriminatory power using the Kolmogorov-Smirnov test, significant feature selection, and stepwise feature selection. The reduction of the feature vector simplifies the complexity of classification scheme and does not impair the classification accuracy. In some cases a decrease of the error rate is noted. Based on this analysis, we present an improved classification system for cancer malignancy grading.