Effect of Image View for Mammogram Mass Classification – An Extreme Learning Based Approach

Abstract

Mammogram images are broadly categorized into two types: carniocaudal (CC) view and mediolateral oblique (MLO) view. In this paper, we study the effect of different image views for mammogram mass classification. For the experiments, we consider a dataset of 328 CC view images and 334 MLO view images (almost equal ratio) from a publicly available film mammogram image dataset [3]. First, features are extracted using a novel radon-wavelet based image descriptor. Then an extreme learning machine (ELM) based classification technique is applied and the performance of five different ELM kernels are compared: sigmoidal, sine, triangular basis, hard limiter and radial basis function. Performances are reported in terms of three important statistical measures namely, sensitivity or true positive rate (TPR), specificity or false negative rate (SPC) and recognition accuracy (ACC). Our experimental outcome for the present setup is two-fold: (i) CC view performs better then MLO for mammogram mass classification, (ii) hard limiter is the best ELM kernel for this problem.