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.

Source: Computers in Biology and Medicine - October 12, 2016 Category: Bioinformatics Authors: Łukasz Jeleń, Adam Krzyżak, Thomas Fevens, Michał Jeleń Source Type: research