TY  - JOUR
Y1  - 2016///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011999811&doi=10.3844%2fajassp.2016.552.561&partnerID=40&md5=6d8acafe2524dbef006c6ea356a5ec6c
A1  - Ibrahim, A.M.
A1  - Baharudin, B.
A1  - Md Said, A.
A1  - Hashimah, P.N.
JF  - American Journal of Applied Sciences
VL  - 13
N2  - 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.
IS  - 5
ID  - scholars7038
SN  - 15469239
PB  - Science Publications
EP  - 561
AV  - none
N1  - cited By 1
SP  - 552
TI  - Classification of breast tumor in mammogram images using unsupervised feature learning
ER  -