Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine

Syam Julio A. Sarosa; Fitri Utaminingrum; Fitra A. Bachtiar

doi:10.1109/SIET.2018.8693146

Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine

Syam Julio A. Sarosa
, Fitri Utaminingrum
, Fitra A. Bachtiar

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

35 Citations (Scopus)

Abstract

Breast cancer is the most common cancer in women. Breast cancer is also the deadliest cancer in women. In 2012, there were 19,750 breast cancer patients in Indonesia dead. There is no sure way to prevent breast cancer. However, the recovery rates and survival rates can be improved by early detection through routine examination. Using X-ray radiation, breast tissue can be obtained. This image is called mammogram. In this paper, a combination of Gray-level co-occurrence matrix (GLCM) and Support Vector Machine (SVM) is used to classify benign-malignant patient based on mammography image. This paper aims to find the optimal GLCM angle for breast cancer classification cases using mammogram data. Mammogram images used in this paper provided by Curated Breast Imaging Subset of Digital Database Screening Mammography (CBIS-DDSM) dataset. From experimental result, obtained accuracy 63.03% and specificity 89.01%.

Original language	English
Title of host publication	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	54-59
Number of pages	6
ISBN (Electronic)	9781538674079
DOIs	https://doi.org/10.1109/SIET.2018.8693146
Publication status	Published - 2 Jul 2018
Event	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Malang, Indonesia Duration: 10 Nov 2018 → 12 Nov 2018

Publication series

Name	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings

Conference

Conference	3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Country/Territory	Indonesia
City	Malang
Period	10/11/18 → 12/11/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast Cancer
GLCM
Mammogram
SVM

Access to Document

10.1109/SIET.2018.8693146

Cite this

Sarosa, S. J. A., Utaminingrum, F., & Bachtiar, F. A. (2018). Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. In 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings (pp. 54-59). Article 8693146 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIET.2018.8693146

Sarosa, Syam Julio A. ; Utaminingrum, Fitri ; Bachtiar, Fitra A. / Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 54-59 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings).

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title = "Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine",

abstract = "Breast cancer is the most common cancer in women. Breast cancer is also the deadliest cancer in women. In 2012, there were 19,750 breast cancer patients in Indonesia dead. There is no sure way to prevent breast cancer. However, the recovery rates and survival rates can be improved by early detection through routine examination. Using X-ray radiation, breast tissue can be obtained. This image is called mammogram. In this paper, a combination of Gray-level co-occurrence matrix (GLCM) and Support Vector Machine (SVM) is used to classify benign-malignant patient based on mammography image. This paper aims to find the optimal GLCM angle for breast cancer classification cases using mammogram data. Mammogram images used in this paper provided by Curated Breast Imaging Subset of Digital Database Screening Mammography (CBIS-DDSM) dataset. From experimental result, obtained accuracy 63.03\% and specificity 89.01\%.",

keywords = "Breast Cancer, GLCM, Mammogram, SVM",

author = "Sarosa, \{Syam Julio A.\} and Fitri Utaminingrum and Bachtiar, \{Fitra A.\}",

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Sarosa, SJA, Utaminingrum, F & Bachtiar, FA 2018, Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings., 8693146, 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 54-59, 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018, Malang, Indonesia, 10/11/18. https://doi.org/10.1109/SIET.2018.8693146

Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. / Sarosa, Syam Julio A.; Utaminingrum, Fitri ; Bachtiar, Fitra A.
3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 54-59 8693146 (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine

AU - Sarosa, Syam Julio A.

AU - Utaminingrum, Fitri

AU - Bachtiar, Fitra A.

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Breast cancer is the most common cancer in women. Breast cancer is also the deadliest cancer in women. In 2012, there were 19,750 breast cancer patients in Indonesia dead. There is no sure way to prevent breast cancer. However, the recovery rates and survival rates can be improved by early detection through routine examination. Using X-ray radiation, breast tissue can be obtained. This image is called mammogram. In this paper, a combination of Gray-level co-occurrence matrix (GLCM) and Support Vector Machine (SVM) is used to classify benign-malignant patient based on mammography image. This paper aims to find the optimal GLCM angle for breast cancer classification cases using mammogram data. Mammogram images used in this paper provided by Curated Breast Imaging Subset of Digital Database Screening Mammography (CBIS-DDSM) dataset. From experimental result, obtained accuracy 63.03% and specificity 89.01%.

AB - Breast cancer is the most common cancer in women. Breast cancer is also the deadliest cancer in women. In 2012, there were 19,750 breast cancer patients in Indonesia dead. There is no sure way to prevent breast cancer. However, the recovery rates and survival rates can be improved by early detection through routine examination. Using X-ray radiation, breast tissue can be obtained. This image is called mammogram. In this paper, a combination of Gray-level co-occurrence matrix (GLCM) and Support Vector Machine (SVM) is used to classify benign-malignant patient based on mammography image. This paper aims to find the optimal GLCM angle for breast cancer classification cases using mammogram data. Mammogram images used in this paper provided by Curated Breast Imaging Subset of Digital Database Screening Mammography (CBIS-DDSM) dataset. From experimental result, obtained accuracy 63.03% and specificity 89.01%.

KW - Breast Cancer

KW - GLCM

KW - Mammogram

KW - SVM

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M3 - Conference contribution

AN - SCOPUS:85065245425

T3 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings

SP - 54

EP - 59

BT - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 10 November 2018 through 12 November 2018

ER -

Sarosa SJA, Utaminingrum F , Bachtiar FA. Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. In 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 54-59. 8693146. (3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings). doi: 10.1109/SIET.2018.8693146

Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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