Research Article Open Access

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Aidarus M. Ibrahim1, Baharum Baharudin1, Abas Md Said1 and P. N. Hashimah2
  • 1 Universiti Teknologi PETRONAS, Malaysia
  • 2 Universiti Teknologi MARA, Malaysia


In this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with well-known feature extraction method namely Bag-of-features. The resulting feature vector is fed to two different classifiers: K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of these classifiers is evaluated to use of Receiver Operating Characteristics (ROC). Our results show that k-means-max pooling, combined with K-NN, achieved good performance with an average classification accuracy of 98.19%, sensitivity of 97.09% and specificity of 99.35%.

American Journal of Applied Sciences
Volume 13 No. 5, 2016, 552-561


Submitted On: 24 December 2015 Published On: 13 May 2016

How to Cite: Ibrahim, A. M., Baharudin, B., Said, A. M. & Hashimah, P. N. (2016). Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning. American Journal of Applied Sciences, 13(5), 552-561.

  • 2 Citations



  • Mammogram
  • Breast Cancer
  • K-Means Clustering
  • Max-Pooling
  • Bag-of-Features
  • Classifier