eprintid: 7038
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/70/38
datestamp: 2023-11-09 16:18:50
lastmod: 2023-11-09 16:18:50
status_changed: 2023-11-09 16:08:19
type: article
metadata_visibility: show
creators_name: Ibrahim, A.M.
creators_name: Baharudin, B.
creators_name: Md Said, A.
creators_name: Hashimah, P.N.
title: Classification of breast tumor in mammogram images using unsupervised feature learning
ispublished: pub
note: cited By 1
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. © 2016 Asad Freihat, Radwan Abu-Gdairi, Hammad Khalil, Eman Abuteen, Mohammed Al-Smadi and Rahmat Ali Khan.
date: 2016
publisher: Science Publications
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011999811&doi=10.3844%2fajassp.2016.552.561&partnerID=40&md5=6d8acafe2524dbef006c6ea356a5ec6c
id_number: 10.3844/ajassp.2016.552.561
full_text_status: none
publication: American Journal of Applied Sciences
volume: 13
number: 5
pagerange: 552-561
refereed: TRUE
issn: 15469239
citation:   Ibrahim, A.M. and Baharudin, B. and Md Said, A. and Hashimah, P.N.  (2016) Classification of breast tumor in mammogram images using unsupervised feature learning.  American Journal of Applied Sciences, 13 (5).  pp. 552-561.  ISSN 15469239