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

  • Sk. Md. ObaidullahEmail author
  • Sajib Ahmed
  • Teresa Gonçalves
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10986)


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.


Breast cancer Mammogram mass classification Image view Image descriptors Extreme learning 



The first and second author of this paper are thankful to Erasmus Leader project funded by European Commission for their post-doctoral and doctoral research study at University of Évora, Portugal. The first author also acknowledges his employer Aliah University for granting study leave for post-doctoral research.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sk. Md. Obaidullah
    • 1
    Email author
  • Sajib Ahmed
    • 1
  • Teresa Gonçalves
    • 1
  1. 1.Department of InformaticsUniversity of ÉvoraEvoraPortugal

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