Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM
Open Access
- 18 May 2021
- journal article
- Published by Institute of Research and Community Services Diponegoro University (LPPM UNDIP) in Jurnal Teknologi dan Sistem Komputer
- Vol. 9 (3), 150-156
- https://doi.org/10.14710/jtsiskom.2021.14104
Abstract
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 %.Keywords
Funding Information
- UIN Sunan Ampel Surabaya Indonesia
This publication has 32 references indexed in Scilit:
- Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic reviewComputer Methods and Programs in Biomedicine, 2018
- Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Dependency Analysis of Accuracy Estimates in k-Fold Cross ValidationIEEE Transactions on Knowledge and Data Engineering, 2017
- Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main OrnamentInternational Journal of Intelligent Systems and Applications, 2017
- The predictive value of methylene blue dye as a single technique in breast cancer sentinel node biopsy: a study from Dharmais Cancer HospitalWorld Journal of Surgical Oncology, 2017
- Risk Factors and Preventions of Breast CancerInternational Journal of Biological Sciences, 2017
- Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening EffectivenessThe New England Journal of Medicine, 2016
- CancersPublished by Springer Science and Business Media LLC ,2016
- Systematic review of 3D mammography for breast cancer screeningThe Breast, 2016
- Traffic sign recognition using HOG-SVM and grid searchPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014