Feature Extraction for Breast Cancer Classification: A Comparative Study for Multiple Subsets of Features

the most doctors spend a large part of their time looking at a benign tissue, which can easily be distinguished from cancer in most cases. This represents a waste of time and resources that could be better spent analyzing patients and to focus on cases where the disease is difficult to determine the classification or served with non-standard features. As a result, many researchers began to develop diagnostic methods of computer-aided through the application of image processing and computer vision techniques in an attempt to determine the spatial location of diseases such as breast cancer. This paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications. In this study, the focus is on the development of classification prototype for the assessment of breast cancer images based on Fine Needle Aspiration. The parts of this study include: image segmentation process, features extraction process, then followed by classification process. The automatic system of malignancy classification was applied on a set of medical images. Three subsets of features (binary, color, and textural) features are used for comparison. Three classifiers (SVM, SOM, and KNN) are used to classify medical data for diagnosis. Color features and KNN classifier show the best accuracy among others.