DOI: https://doi.org/10.14710/jtsiskom.2021.14104

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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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 %.

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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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Section: Original Research Articles
Language : EN
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  1. A. Deverakonda and N. Gupta, “Diagnosis and treatment of cervical cancer : A review,” Research and Reviews Journal of Medical & Health Sciences, vol. 5, no. 3, pp. 1–11, 2016
  2. R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2019,” CA: A Cancer Journal for Clinicians, vol. 69, no. 1, pp. 7–34, 2019. doi: 10.3322/caac.21551
  3. E. Weisshaar, “Cancers,” in Pruritus, 2nd Ed. Cham: Springer, 2016, pp. 283–287. doi: 10.1007/978-3-319-33142-3_37
  4. H. Sung et al., “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal of Clinicians, vol. 71, no. 2, pp. 209-249, 2021. doi: 10.3322/caac.21660
  5. B. Brahma et al., “The predictive value of methylene blue dye as a single technique in breast cancer sentinel node biopsy: A study from Dharmais Cancer Hospital,” World Journal of Surgical Oncology, vol. 15, no. 1, pp. 1–7, 2017. doi: 10.1186/s12957-017-1113-8
  6. M. Dewi, “Sebaran kanker di Indonesia, riset kesehatan dasar 2007,” Indonesian Journal of Cancer, vol. 11, no. 1, pp. 1–8, 2017
  7. Y. S. Sun et al., “Risk factors and preventions of breast cancer,” International Journal of Biological Sciences, vol. 13, no. 11, pp. 1387–1397, 2017. doi: 10.7150/ijbs.21635
  8. S. C. Harvey et al., “Systematic review of 3D mammography for breast cancer screening,” The Breast, vol. 27, pp. 52–61, 2016. doi: 10.1016/j.breast.2016.01.002
  9. H. G. Welch, P. C. Prorok, A. J. O’Malley, and B. S. Kramer, “Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness,” New England Journal of Medicine, vol. 375, no. 15, pp. 1438–1447, 2016. doi: 10.1056/NEJMoa1600249
  10. N. I. R. Yassin, S. Omran, E. M. F. El Houby, and H. Allam, “Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review,” Computer Methods and Programs in Biomedicine, vol. 156, pp. 25–45, 2018. doi: 10.1016/j.cmpb.2017.12.012
  11. D. C. R. Novitasari, A. Z. Foeady, M. Thohir, A. Z. Arifin, K. Niam, and A. H. Asyhar, “Automatic approach for cervical cancer detection based on deep belief network (DBN) using colposcopy data,” in International Conference on Artificial Intelligence in Information and Communication, Fukuoka, Japan, Feb. 2020, pp. 415–420. doi: 10.1109/ICAIIC48513.2020.9065196
  12. N. Nurajijah and D. Riana, “Algoritma naïve bayes, decision tree, dan SVM untuk klasifikasi persetujuan pembiayaan nasabah koperasi syariah,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 2, pp. 77–82, 2019. doi: 10.14710/jtsiskom.7.2.2019.77-82
  13. N. Z. Kurniawan, S. T. Rasmana, and Y. Triwidyastuti, “Identifikasi jenis penyakit daun tembakau menggunakan metode gray level co-occurrence matrix (GLCM) dan support vector machine (SVM),” JCONES Journal of Control and Network Systems, vol. 3, no. 1, pp. 46–53, 2016
  14. R. Vijayarajeswari, P. Parthasarathy, S. Vivekanandan, and A. A. Basha, “Classification of mammogram for early detection of breast cancer using SVM classifier and hough transform,” Measurement: Journal of the International Measurement Confederation, vol. 146, pp. 800–805, 2019. doi: 10.1016/j.measurement.2019.05.083
  15. F. Ma’arif and T. Arifin, “Optimasi fitur menggunakan backward elimination dan algoritma SVM untuk klasifikasi kanker payudara,” Jurnal Informatika, vol. 4, no. 1, pp. 46–53, 2017
  16. M. Nixon and A. Aguado, Feature extraction and image processing for computer vision, 5th ed. London: Academic press, 2019. doi: B978-0-12-814976-8.00003-8
  17. E. F. Tunjungsari, R. Apsari, and E. Purwanti, “Deteksi dini kanker payudara dari citra mammografi menggunakan gray level co-occurence matrices (GLCM) dan fuzzy backpropagation,” Jurnal Fisika dan Terapannya, vol. 4, no. 1, pp. 81–94, 2016
  18. S. J. A. Sarosa, F. Utaminingrum, and F. A. Bachtiar, “Mammogram breast cancer classification using gray-level co-occurrence matrix and support vector machine,” in International Conference on Sustainable Information Engineering and Technology, Malang, Indonesia, Nov. 2018, pp. 54–59. doi: 10.1109/SIET.2018.8693146
  19. R. Suresh, A. N. Rao, and B. E. Reddy, “Detection and classification of normal and abnormal patterns in mammograms using deep neural network,” Concurrency Computation, vol. 31, no. 14, pp. 1–12, 2019. doi: 10.1002/cpe.5293
  20. A. H. Farhan and M. Y. Kamil, “Texture analysis of mammogram using histogram of oriented gradients method,” IOP Conference Series: Materials Science and Engineering, vol. 881, 012149, 2020. doi: 10.1088/1757-899X/881/1/012149
  21. M. S. Wibawa and K. D. P. Novianti, “Reduksi fitur untuk optimalisasi klasifikasi tumor payudara berdasarkan data citra FNA,” in Konferensi Nasional Sistem & Informatika, Bali, Indonesia, Aug. 2017, pp. 73–78
  22. W. Ma et al., “Breast cancer molecular subtype prediction by mammographic radiomic features,” Academic Radiology, vol. 26, no. 2, pp. 196–201, 2019. doi: 10.1016/j.acra.2018.01.023
  23. K. C. Tatikonda, C. M. Bhuma, and S. K. Samayamantula, “The analysis of digital mammograms using HOG and GLCM features,” in International Conference on Computing, Communication and Networking Technologies, Bengaluru, India, Jul. 2018, pp. 1–7. doi: 10.1109/ICCCNT.2018.8493809
  24. J. Brannen, Mixing methods: Qualitative and quantitative research. New York: Routledge, 2017. doi: 10.4324/9781315248813
  25. J. Suckling et al., “Mammographic Image Analysis Society (MIAS) database v1.21 [Dataset],” Apollo, 2015. Available: https://www.repository.cam.ac.uk/ handle/1810/250394
  26. D. C. R. Novitasari, “Klasifikasi alzheimer dan non alzheimer menggunakan fuzzy c-mean, gray level co-occurence matrix dan support vector machine,” Jurnal Matematika “MANTIK,” vol. 4, no. 2, pp. 83–89, 2018. doi: 10.15642/mantik.2018.4.2.83-89
  27. R. Venkateswari, “Brain tumor segmentation based on GLCM feature extraction using probabilistic neural network,” International Journal of Engineering Science and Computing, vol. 7, no. 7, pp. 14031–14035, 2017
  28. D. Alamsyah, “Pengenalan mobil pada citra digital menggunakan HOG-SVM,” Jatisi, vol. 1, no. 2, pp. 162–168, 2017
  29. F. D. Adhinata, M. Ikhsan, and W. Wahyono, “People counter on CCTV video using histogram of oriented gradient and kalman filter methods,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 222–227, 2020. doi: 10.14710/jtsiskom.2020.13660
  30. S. A. Korkmaz, A. Akcicek, H. Binol, and M. F. Korkmaz, “Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features,” in IEEE International Symposium on Intelligent Systems and Informatics, Subotica, Serbia, Sept. 2017, pp. 339–342. doi: 10.1109/SISY.2017.8080578
  31. A. Z. Foeady, D. C. R. Novitasari, A. H. Asyhar, and M. Firmansjah, “Automated diagnosis system of diabetic retinopathy using GLCM method and SVM classifier,” in International Conference on Electrical Engineering, Computer Science and Informatics, Malang, Indonesia, Oct. 2018, pp. 154–160. doi: 10.1109/EECSI.2018.8752726
  32. D. C. R. Novitasari, A. Lubab, A. Sawiji, and A. H. Asyhar, “Application of feature extraction for breast cancer using one order statistic, GLCM, GLRLM, and GLDM,” Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 115–120, 2019. doi: 10.25046/aj040413
  33. D. C. R. Novitasari, A. H. Asyhar, M. Thohir, A. Z. Arifin, H. Mu’jizah, and A. Z. Foeady, “Cervical cancer identification based texture analysis using GLCM-KELM on colposcopy data,” in International Conference on Artificial Intelligence in Information and Communication, Fukuoka, Japan, Feb. 2020, pp. 409–414. doi: 10.1109/ICAIIC48513.2020.9065252
  34. M. Thohir, A. Z. Foeady, D. C. R. Novitasari, A. Z. Arifin, B. Y. Phiadelvira, and A. H. Asyhar, “Classification of colposcopy data using GLCM-SVM on cervical cancer,” in International Conference on Artificial Intelligence in Information and Communication, Fukuoka, Japan, Feb. 2020, pp. 373–378, 2020. doi: 10.1109/ICAIIC48513.2020.9065027
  35. P. S. S. Kumar and V. S. Dharun, “Extraction of texture features using GLCM and shape features using connected regions,” International Journal of Engineering and Technology, vol. 8, no. 6, pp. 2926–2930, 2016. doi: 10.21817/ijet/2016/v8i6/160806254
  36. A. A. Kasim, R. Wardoyo, and A. Harjoko, “Batik classification with artificial neural network based on texture-shape feature of main ornament,” International Journal of Intelligent Systems and Applications, vol. 9, no. 6, pp. 55–65, 2017. doi: 10.5815/ijisa.2017.06.06
  37. L. Ratnawati and D. R. Sulistyaningrum, “Penerapan random forest untuk mengukur tingkat keparahan penyakit pada daun apel,” Jurnal Sains dan Seni ITS, vol. 8, no. 2, pp. A71–A77, 2019
  38. D. C. R. Novitasari et al., “Whirlwind classification with imbalanced upper air data handling using SMOTE algorithm and SVM classifier,” Journal of Physics: Conference Series, vol. 1501, no. 1, 012010, 2020. doi: 10.1088/1742-6596/1501/1/012010
  39. D. C. R. Novitasari et al., “Detection of COVID-19 chest x-ray using support vector machine and convolutional neural network,” Communications in Mathematical Biology and Neuroscience, vol. 2020, pp. 1–19, 2020. doi: 10.28919/cmbn/4765
  40. D. C. R. Novitasari, M. F. Rozi, and R. Veriani, “Klasifikasi kelainan pada jantung melalui citra iris mata menggunakan fuzzy c-means sebagai pengambil fitur iris dan klasifikasi menggunakan support vector machine,” INTEGER: Journal of Information Technology, vol. 4, no. 1, pp. 1–10, 2019. doi: 10.31284/j.integer.2019.v4i1.489
  41. T.-T. Wong and N.-Y. Yang, “Dependency analysis of accuracy estimates in k-fold cross validation,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 11, pp. 2417–2427, 2017. doi: 10.1109/TKDE.2017.2740926
  42. C. Yao, F. Wu, H. J. Chen, X. L. Hao, and Y. Shen, “Traffic sign recognition using hog-svm and grid search,” in International Conference on Signal Processing, Hangzhou, China, Oct. 2014, 1, pp. 962–965. doi: 10.1109/ICOSP.2014.7015147
  43. A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi algoritme support vector machine (SVM) untuk prediksi ketepatan waktu kelulusan mahasiswa,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, pp. 1704–1708, 2018
  44. P. K. Intan, “Comparison of kernel function on support vector machine in classification of childbirth,” Jurnal Matematika MANTIK, vol. 5, no. 2, pp. 90–99, 2019. doi: 10.15642/mantik.2019.5.2.90-99
  45. A. Bakhshipour and A. Jafari, “Evaluation of support vector machine and artificial neural networks in weed detection using shape features,” Computers and Electronics in Agriculture, vol. 145, no. 2017, pp. 153–160, 2018. doi: 10.1016/j.compag.2017.12.032
  46. R. R. Waliyansyah, K. Adi, and J. E. Suseno, “Implementasi metode gray level co-occurrence matrix dalam identifikasi jenis daun tengkawang,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 7, no. 1, pp. 50–56, 2018. doi: 10.22146/jnteti.v7i1.400

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