%0 Journal Article
%@ 15469239
%A Ibrahim, A.M.
%A Baharudin, B.
%A Md Said, A.
%A Hashimah, P.N.
%D 2016
%F scholars:7038
%I Science Publications
%J American Journal of Applied Sciences
%N 5
%P 552-561
%R 10.3844/ajassp.2016.552.561
%T Classification of breast tumor in mammogram images using unsupervised feature learning
%U https://khub.utp.edu.my/scholars/7038/
%V 13
%X 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 wellknown feature extraction method namely Bag-of-features. The resulting feature vector is fed to two different classifiers: K-Nearest Neighbor (KNN) 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. © 2016 Asad Freihat, Radwan Abu-Gdairi, Hammad Khalil, Eman Abuteen, Mohammed Al-Smadi and Rahmat Ali Khan.
%Z cited By 1