Application of feature extraction for breast cancer using one order statistic, glcm, glrlm, and gldm

Novitasari, Dian Candra Rini and Lubab, Ahmad and Sawiji, Asri and Asyhar, Ahmad Hanif (2019) Application of feature extraction for breast cancer using one order statistic, glcm, glrlm, and gldm. Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 4 (4). pp. 115-120. ISSN 2415-6698

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Abstract

The increasing number of breast cancer in recent years has attracted numerous researchers’ attention. Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer. The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer. In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image. Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics. The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification. The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.

Item Type: Article
Creators:
Creators
Email
NIDN
Novitasari, Dian Candra Rini
diancrini@uinsby.ac.id
2024118502
Lubab, Ahmad
ahmadlubab@uinsby.ac.id
2011118102
Sawiji, Asri
sawiji.asri@uinsby.ac.id
2026068701
Asyhar, Ahmad Hanif
hanif@uinsby.ac.id
-
Uncontrolled Keywords: Breast Cancer; feature extraction; GLCM; GLRLM; GLDM
Subjects: 06 BIOLOGICAL SCIENCES > 0601 Biochemistry and Cell Biology > 060103 Cell Development, Proliferation and Death
06 BIOLOGICAL SCIENCES > 0604 Genetics > 060406 Genetic Immunology
Divisions: Karya Ilmiah > Artikel
Depositing User: Samidah Nurmayuni
Date Deposited: 21 Jul 2021 10:30
Last Modified: 21 Jul 2021 10:30
URI: http://repository.uinsby.ac.id/id/eprint/453

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