Classification of breast tumor in mammogram images using unsupervised feature learning
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 u...
| Main Authors: | Ibrahim, A.M., Baharudin, B., Md Said, A., Hashimah, P.N. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30866 / |
| Published: |
Science Publications
2016
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011999811&doi=10.3844%2fajassp.2016.552.561&partnerID=40&md5=6d8acafe2524dbef006c6ea356a5ec6c http://eprints.utp.edu.my/30866/ |
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| Summary: |
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. |
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