Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images
Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) coul...
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| Format: | Article |
| Language: | English |
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Poltekkes Kemenkes Surabaya
2022-12-01
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| Series: | Jurnal Teknokes |
| Subjects: | |
| Online Access: | http://teknokes.poltekkesdepkes-sby.ac.id/index.php/Teknokes/article/view/458 |
| _version_ | 1797974552644419584 |
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| author | Ega Elfira Wahmisari Priharti Dien Rahmawati |
| author_facet | Ega Elfira Wahmisari Priharti Dien Rahmawati |
| author_sort | Ega Elfira |
| collection | DOAJ |
| description | Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%. |
| first_indexed | 2024-04-11T04:21:36Z |
| format | Article |
| id | doaj.art-59db56a94820497c879231acad7883e2 |
| institution | Directory Open Access Journal |
| issn | 2407-8964 |
| language | English |
| last_indexed | 2024-04-11T04:21:36Z |
| publishDate | 2022-12-01 |
| publisher | Poltekkes Kemenkes Surabaya |
| record_format | Article |
| series | Jurnal Teknokes |
| spelling | doaj.art-59db56a94820497c879231acad7883e22022-12-30T14:06:13ZengPoltekkes Kemenkes SurabayaJurnal Teknokes2407-89642022-12-01154197–205197–20510.35882/teknokes.v15i4.458458Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram ImagesEga Elfira0Wahmisari Priharti1Dien Rahmawati2School of Electrical Engineering, Telkom University, IndonesiaSchool of Electrical Engineering, Telkom University, IndonesiaSchool of Electrical Engineering, Telkom University, IndonesiaBreast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%.http://teknokes.poltekkesdepkes-sby.ac.id/index.php/Teknokes/article/view/458breast cancermammogram imagemachine learningsupport vector machinesgray level co-occurrence matrixfirst order |
| spellingShingle | Ega Elfira Wahmisari Priharti Dien Rahmawati Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images Jurnal Teknokes breast cancer mammogram image machine learning support vector machines gray level co-occurrence matrix first order |
| title | Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images |
| title_full | Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images |
| title_fullStr | Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images |
| title_full_unstemmed | Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images |
| title_short | Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images |
| title_sort | comparison of glcm and first order feature extraction methods for classification of mammogram images |
| topic | breast cancer mammogram image machine learning support vector machines gray level co-occurrence matrix first order |
| url | http://teknokes.poltekkesdepkes-sby.ac.id/index.php/Teknokes/article/view/458 |
| work_keys_str_mv | AT egaelfira comparisonofglcmandfirstorderfeatureextractionmethodsforclassificationofmammogramimages AT wahmisaripriharti comparisonofglcmandfirstorderfeatureextractionmethodsforclassificationofmammogramimages AT dienrahmawati comparisonofglcmandfirstorderfeatureextractionmethodsforclassificationofmammogramimages |