skip to main content

IMPLEMENTASI FILTER GRAY LEVEL CO-OCCURANCE MATRIKS TERHADAP SISTEM KLASIFIKASI KANKER PAYUDARA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

*Muhammad Ariefur Rohman  -  Jurusan Teknik Elektro, Universitas Brawijaya Malang, Indonesia
Panca Mudjirahardjo  -  Jurusan Teknik Elektro, Universitas Brawijaya Malang, Indonesia
M. Aziz Muslim  -  Jurusan Teknik Elektro, Universitas Brawijaya Malang, Indonesia
Dikirim: 18 Jun 2021; Diterbitkan: 30 Agu 2021.
Akses Terbuka Copyright (c) 2021 Transmisi: Jurnal Ilmiah Teknik Elektro under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Sari

 Kanker payudara merupakan salah satu penyakit dengan proyeksi kematian terbesar selama 10 tahun terakhir dengan indeks kematian mencapai rata-rata 5 juta per-tahun, dan diprediksi akan terus naik hingga 60% di seluruh dunia. Umumnya, banyak metode yang digunakan untuk mendeteksi penyakit ini, salah satunya dengan mengamati jaringan histopatology. Banyak dari para ilmuwan, yang menggunakan jaringan histopatology untuk menganalisa, mengamati dan membuat sistem klasifikasi kanker payudara dengan berbagai metode, seperti: convolutional neural network, deep learning, support vector machine. Penggunaan metode convolutional neural network terbukti paling unggul pada sistem klasifikasi kanker payudara, namun akurasi rata-rata yang dihasilkan relatif cukup rendah. Selain itu, penggunaan metode convolutional neural network, membutuhkan waktu komputasi yang relatif lama untuk mengklasifikasikan 7909 dataset ukuran 4 GB. Berdasarkan alasan tersebut, desain sebuah sistem klasifikasi dengan mengimplementasikan Gray Level Co-occurance Matrix pada saat prapengolahan data CNN di butuhkan. Hasil penelitian menunjukkan bahwa, penggunaan  metode  CNN  menghasilkan  waktu  komputasi  lebih  lama,  yaitu:  3300  detik  dibandingkan  dengan kombinasi metode GLCM Entropy dan CNN 2040 detik. Sedangkan rata-rata akurasi latih dan uji yang dihasilkan oleh metode kombinasi GLCM entropy dan CNN adalah  92,26% dan 94,16%, lebih unggul dibandingkan dengan metode CNN, yaitu: 88,41% untuk data latih, dan 87,68% untuk data uji.

Fulltext View|Download
Kata Kunci: Convolutional neural network; gray level co-occurance matrix; breast cancer; neural network;

Article Metrics:

  1. . ACS - American Cancer Society, Breast cancer Facts & Figures 2019-2020. Atlanta: American Cancer Society, Inc., 2019
  2. . Y. Azhar, H. Agustina, M. Abdurahman, and D. Achmad, “Breast Cancer in West Java: Where Do We Stand and Go?,” Indones. J. Cancer, vol. 14, no. 3, p. 91, 2020, doi: 10.33371/ijoc.v14i3.737
  3. . L. Anggorowati, “Faktor Risiko Kanker Payudara Wanita,” KEMAS J. Kesehat. Masy., vol. 8, no. 2, pp. 121–126, 2013, doi: 10.15294/kemas.v8i2.2635
  4. . Y. S. Sun et al., “Risk factors and preventions of breast cancer,” Int. J. Biol. Sci., vol. 13, no. 11, pp. 1387–1397, 2017, doi: 10.7150/ijbs.21635
  5. . E. J. Watkins, “Overview of breast cancer,” J. Am. Acad. Physician Assist., vol. 32, no. 10, pp. 13–17, 2019, doi: 10.1097/01.JAA.0000580524.95733.3d
  6. . S. H. Bhandari, “A bag-of-features approach for malignancy detection in breast histopathology images,” Proc. - Int. Conf. Image Process. ICIP, vol. 2015-Decem, no. Ilc, pp. 4932–4936, 2015, doi: 10.1109/ICIP.2015.7351745
  7. . A. D. Belsare, M. M. Mushrif, M. A. Pangarkar, and N. Meshram, “Classification of breast cancer histopathology images using texture feature analysis,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2016-Janua, pp. 1–5, 2016, doi: 10.1109/TENCON.2015.7372809
  8. . K. Das, S. P. K. Karri, A. Guha Roy, J. Chatterjee, and D. Sheet, “Classifying histopathology whole- slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification,” Proc. - Int. Symp. Biomed. Imaging, pp. 1024–1027, 2017, doi: 10.1109/ISBI.2017.7950690
  9. . B. Ehteshami Bejnordi et al., “Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images,” Proc. - Int. Symp. Biomed. Imaging 2017, pp. 929–932, 2017, doi: 10.1109/ISBI.2017.7950668
  10. . S. K. Jafarbiglo, H. Danyali, and M. S. Helfroush, “Nuclear Atypia Grading in Histopathological Images of Breast Cancer Using Convolutional Neural Networks,” Proc. - 2018 4th Iran. Conf. Signal Process. Intell. Syst. ICSPIS 2018, pp. 89–93, 2018, doi: 10.1109/ICSPIS.2018.8700540
  11. . S. A. Adeshina, A. P. Adedigba, A. A. Adeniyi, and A. M. Aibinu, “Breast cancer histopathology image classification with deep convolutional neural networks,” 14th Int. Conf. Electron. Comput. Comput. ICECCO 2018, pp. 206–212, 2019, doi: 10.1109/ICECCO.2018.8634690
  12. . P. T. Nguyen, T. T. Nguyen, N. C. Nguyen, and T. T. Le, “Multiclass Breast Cancer Classification Using Convolutional Neural Network,” Proc. - 2019 Int. Symp. Electr. Electron. Eng. ISEE 2019, pp. 130–134, 2019, doi: 10.1109/ISEE2.2019.8920916
  13. . F. A. Spanhol, P. R. Cavalin, L. S. Oliveira, C. Petitjean, and L. Heutte, “Deep features for breast cancer histopathological image classification,” 2017 IEEE Int. Conf. Syst. Man, Cybern. SMC 2017, vol. 2017-Janua, pp. 1868–1873, 2017, doi: 10.1109/SMC.2017.8122889
  14. . V. College et al., “Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network,” Procedia Comput. Sci., vol. 59, no. 7, pp. 83–91, 2015, doi: 10.1016/j.procs.2015.07.340
  15. . S. Sharma, M. Kaur, and D. Saini, “Lung cancer detection using convolutional neural network,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 3256–3262, 2019, doi: 10.35940/ijeat.F8836.088619
  16. . J. Tan et al., “Glcm-cnn: Gray level co-occurrence matrix based cnn model for polyp diagnosis,” 2019 IEEE EMBS Int. Conf. Biomed. Heal. Informatics, BHI 2019 - Proc., pp. 1–4, 2019, doi: 10.1109/BHI.2019.8834585
  17. . F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A Dataset for Breast Cancer Histopathological Image Classification,” IEEE Trans. Biomed. Eng., vol. 63, no. 7, pp. 1455–1462, 2016, doi: 10.1109/TBME.2015.2496264
  18. . N. Mehdiyev, D. Enke, P. Fettke, and P. Loos, “Evaluating Forecasting Methods by Considering Different Accuracy Measures,” Procedia Comput. Sci., vol. 95, pp. 264–271, 2016, doi: 10.1016/j.procs.2016.09.332
  19. . K. Adi, C. E. Widodo, A. P. Widodo, R. Gernowo, A. Pamungkas, and R. A. Syifa, “Detection lung cancer using Gray Level Co-Occurrence Matrix (GLCM) and back propagation neural network classification,” J. Eng. Sci. Technol. Rev., vol. 11, no. 2, pp. 8–12, 2018, doi: 10.25103/jestr.112.02
  20. . M. Benco, R. Hudec, P. Kamencay, M. Zachariasova, and S. Matuskal, “An advanced approach to extraction of colour texture features based on GLCM,” Int. J. Adv. Robot. Syst., vol. 11, no. 1, 2014, doi: 10.5772/58692
  21. . Ş. Öztürk and B. Akdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 40–46, 2018, doi: 10.1016/j.procs.2018.05.057
  22. . D. T. A. Alasadi and W. R. Baiee, “Analysis of GLCM Feature Extraction for Choosing Appropriate Angle Relative to BP Classifier,” IOSR J. Comput. Eng., vol. 16, no. 1, pp. 65–69, 2014, doi: 10.9790/0661-16176569
  23. . H. Salman, J. Grover, and T. Shankar, “Hierarchical Reinforcement Learning for Sequencing Behaviors,” vol. 2733, pp. 2709–2733, 2018, doi: 10.1162/NECO
  24. . S. Sakib, Ahmed, A. Jawad, J. Kabir, and H. Ahmed, “An Overview of Convolutional Neural Network: Its Architecture and Applications,” ResearchGate, no. November, 2018, doi: 10.20944/preprints201811.0546.v1
  25. . R. P. Bunker and F. Thabtah, “A machine learning framework for sport result prediction,” Appl. Comput. Informatics, vol. 15, no. 1, pp. 27–33, 2019, doi: 10.1016/j.aci.2017.09.005

Last update:

No citation recorded.

Last update: 2024-04-12 18:45:43

No citation recorded.