Breast Cancer Classification: Comparative Performance Analysis of Image Shape-Based Features and Microarray Gene Expression Data

Abstract

Recently, there has been greater attention to the use of classifier systems in medical diagnosis. Medical diagnostic tools provide automated procedures for objective decisions by making use of quantitative measures and machine learning techniques. These tools are effective and helpful for medical experts to diagnose diseases. One of such diseases is breast cancer which is the second largest cause of cancer deaths among women. To build an intelligent tool, it is very important to have an effective set of features. Two types of feature sets have been commonly implemented for the purpose of breast cancer diagnosis: image shape-based features and microarray gene expression data. Both types of feature sets have been widely implemented; however, there has been no work that directly compared the classification performance of these two feature sets. In this paper, we intensively review related works that used both types of feature sets and we also review the implemented machine learning algorithms. Moreover, we run extensive experiments to compare the classification performance of the aforementioned feature sets. Our results show that the image shape-based features are more discriminative for breast cancer classification when tested with ten-fold cross validation. To check the robustness of the best performing feature set, we further examine it with five-fold cross validation and with a variety of generative classification algorithms.