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

Department of Mathematic, UIN Sunan Ampel Surabaya. Jl. Ahmad Yani No. 117, Jemur Wonosari, Surabaya 60237, Indonesia

Received: 12 Feb 2021; Revised: 4 May 2021; Accepted: 18 May 2021; Available online: 15 Jun 2021; Published: 31 Jul 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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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 %.

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Supplementary Data: Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM
Subject Datasets and results of mammography image analysis for breast cancer classification
Type Data Analysis
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Keywords: breast cancer; HOG; GLCM; shape feature extraction; SVM
Funding: UIN Sunan Ampel Surabaya Indonesia

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