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Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications

A novel Computer Aided Diagnosis (CADx) component is proposed for breast cancer classifications. Four major phases were conducted in this research. The first phase is pre-processing, this is followed by features extraction phase by using the Speed Up Robust Features (SURF). The next phase is feature...

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Main Authors: Salleh, S., Mahmud, R., Rahman, H., Yasiran, S. S.
Format: Article
Published: Univ El Oued, Fac Science & Technology 2017
Subjects:
L Education (General)
Q Science (General)
T58.5-58.64 Information technology
Online Access:http://eprints.utm.my/id/eprint/77516/
http://dx.doi.org/10.4314/jfas.v9i5s.44
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id my.utm.77516
record_format eprints
spelling my.utm.775162022-01-31T08:41:49Z http://eprints.utm.my/id/eprint/77516/ Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications Salleh, S. Mahmud, R. Rahman, H. Yasiran, S. S. L Education (General) Q Science (General) T58.5-58.64 Information technology A novel Computer Aided Diagnosis (CADx) component is proposed for breast cancer classifications. Four major phases were conducted in this research. The first phase is pre-processing, this is followed by features extraction phase by using the Speed Up Robust Features (SURF). The next phase is features selection by using the Principal Component Analysis (PCA). The final phase is the classification phase to classify the cancer. Three different classifiers; Support Vector Machine (SVM). Linear Discriminant Analysis (LDA) and Decision Tree (DT) were compared in this research. Results obtained shows that the PC A-SVM performs the highest accuracy with 92.9% accurate compared to other classifiers. Univ El Oued, Fac Science & Technology 2017 Article PeerReviewed Salleh, S. and Mahmud, R. and Rahman, H. and Yasiran, S. S. (2017) Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications. Journal of Fundamental and Applied Sciences, 9 (5, SI). pp. 624-643. ISSN 2289-5981 http://dx.doi.org/10.4314/jfas.v9i5s.44 DOI:10.4314/jfas.v9i5s.44
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic L Education (General)
Q Science (General)
T58.5-58.64 Information technology
spellingShingle L Education (General)
Q Science (General)
T58.5-58.64 Information technology
Salleh, S.
Mahmud, R.
Rahman, H.
Yasiran, S. S.
Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
description A novel Computer Aided Diagnosis (CADx) component is proposed for breast cancer classifications. Four major phases were conducted in this research. The first phase is pre-processing, this is followed by features extraction phase by using the Speed Up Robust Features (SURF). The next phase is features selection by using the Principal Component Analysis (PCA). The final phase is the classification phase to classify the cancer. Three different classifiers; Support Vector Machine (SVM). Linear Discriminant Analysis (LDA) and Decision Tree (DT) were compared in this research. Results obtained shows that the PC A-SVM performs the highest accuracy with 92.9% accurate compared to other classifiers.
format Article
author Salleh, S.
Mahmud, R.
Rahman, H.
Yasiran, S. S.
author_facet Salleh, S.
Mahmud, R.
Rahman, H.
Yasiran, S. S.
author_sort Salleh, S.
title Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
title_short Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
title_full Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
title_fullStr Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
title_full_unstemmed Speed up robust features (SURF) with principal component analysis-support vector machine (PCA-SVM) for benign and malignant classifications
title_sort speed up robust features (surf) with principal component analysis-support vector machine (pca-svm) for benign and malignant classifications
publisher Univ El Oued, Fac Science & Technology
publishDate 2017
url http://eprints.utm.my/id/eprint/77516/
http://dx.doi.org/10.4314/jfas.v9i5s.44
_version_ 1724073269323104256
score 13.211869

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