Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Jurnal Teknologi dan Sistem Komputer

View Publication Info
 
 
Field Value
 
Title Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM
 
Creator Mu'jizah, Hanimatim
Novitasari, Dian Candra Rini
 
Subject breast cancer; HOG; GLCM; shape feature extraction; SVM
 
Description Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.
 
Publisher Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
 
Date 2021-07-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://jtsiskom.undip.ac.id/article/view/14104
10.14710/jtsiskom.2021.14104
 
Source Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 3, Year 2021 (July 2021); 150-156
Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 3, Year 2021 (July 2021); 150-156
2338-0403
 
Language eng
 
Relation https://jtsiskom.undip.ac.id/article/view/14104/12694
https://jtsiskom.undip.ac.id/article/downloadSuppFile/14104/660
 
Rights Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
https://creativecommons.org/licenses/by-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library