@article{scholars7038,
          number = {5},
            note = {cited By 1},
          volume = {13},
             doi = {10.3844/ajassp.2016.552.561},
           title = {Classification of breast tumor in mammogram images using unsupervised feature learning},
            year = {2016},
       publisher = {Science Publications},
         journal = {American Journal of Applied Sciences},
           pages = {552--561},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011999811&doi=10.3844\%2fajassp.2016.552.561&partnerID=40&md5=6d8acafe2524dbef006c6ea356a5ec6c},
        abstract = {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. {\^A}{\copyright} 2016 Asad Freihat, Radwan Abu-Gdairi, Hammad Khalil, Eman Abuteen, Mohammed Al-Smadi and Rahmat Ali Khan.},
            issn = {15469239},
          author = {Ibrahim, A. M. and Baharudin, B. and Md Said, A. and Hashimah, P. N.}
}