TY  - JOUR
AU  - Ibrahim, Aidarus M. 
AU  - Baharudin, Baharum 
AU  - Said, Abas Md 
AU  - Hashimah, P. N. 
PY  - 2016
TI  - Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning
JF  - American Journal of Applied Sciences
VL  - 13
IS  - 5
DO  - 10.3844/ajassp.2016.552.561
UR  - https://thescipub.com/abstract/ajassp.2016.552.561
AB  - 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%.