DOI: https://doi.org/10.14710/jtsiskom.2021.13943

Segmentasi dan pengorakan citra mikroskopik Pap smear menggunakan algoritme K-means dan J48

Segmentation and analysis of Pap smear microscopic images using the K-means and J48 algorithms

1Department of Information System, STMIK Nusa Mandiri, Jl. Raya Jatiwaringin No 2, Cipinang Melayu, Makasar, Jakarta Timur 13620, Indonesia

2Department of Computer Science, STMIK Nusa Mandiri, Jl. Raya Jatiwaringin No 2, Cipinang Melayu, Makasar, Jakarta Timur 13620, Indonesia

Received: 4 Oct 2020; Revised: 15 Dec 2020; Accepted: 5 Mar 2021; Available online: 20 Apr 2021; Published: 30 Apr 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.

Citation Format:
Abstract
A Pap smear is used to early detection cervical cancer. This study proposes the segmentation and analysis method of Pap smear cells images using the K-means algorithm so that cytoplasmic cells, nuclear cells, and inflammatory cells can be segmented automatically. The results of the feature analysis from the cytoplasmic, nuclear, and inflammatory cell images were classified using the J48 algorithm with 37 training data. The training resulted in an accuracy of 94.594 %, precision of 95 %, and sensitivity of 94.6 %. The classification of 24 testing images resulted in an accuracy of 91.6%, a precision of 92.5 %, and a sensitivity of 91.7 %.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Results
Segmentation results of training data
Subject
Type Research Results
  Download (3MB)    Indexing metadata
Email colleagues
Keywords: k-means; GLCM; J48; pap smear; segmentation
Funding: Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Traffic sign recognition using convolutional neural networks Three combination value of extraction features on GLCM for detecting pothole and asphalt road Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests Course scheduling optimization using genetic algorithm and tabu search Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM More related articles
Most cited articles
Aplikasi Pengolahan Citra Digital Meat Detection Dengan Metode Segmentasi K-Mean Clustering Berbasis OpenCV Dan Eclipse Identification of Rupiah Paper Currency Denomination using SURF and FLANN Methods at Rotation Variation Web Application of Lecturer Performance Assesment on Teaching Process: A Case Study at Internal Quality Assurance Agency of Institut Teknologi Padang Mobile Application of Expert System for Meningitis Early Diagnosis PSS Tuning on Power Generator System using Flower Pollination Algorithm More cited articles
  1. Infodatin, “Beban kanker di Indonesia,” Pusat Data dan Informasi Kementerian Kesehatan RI, 2019. [Online]. Available: https://pusdatin.kemkes.go.id
  2. S. Anggraeni, “Self efficacy wanita usia subur untuk melakukan pap smear ditinjau dari pengetahuan dan dukungan suami,” Viva Medika, vol. 10, no. 18, pp. 86–93, 2017
  3. R. S. D. Wijaya, A. Adiwijaya, A. B. Suksmono, and T. L. R. Mengko, “Segmentasi citra kanker serviks menggunakan markov random field dan algoritma K-means,” Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), vol. 5, no. 1, pp. 139-147, 2021. doi: 10.29207/resti.v5i1.2816
  4. L. Zhao et al., “Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF,” Computers in Biology and Medicine., vol. 71, pp. 46–56, 2016. doi: 10.1016/j.compbiomed.2016.01.025
  5. Y. Song, L. Zhang, S. Chen, D. Ni, B. Lei, and T. Wang, “Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 10, pp. 2421–2433, 2015. doi: 10.1109/TBME.2015.2430895
  6. D. Riana, M. E. Plissiti, C. Nikou, D. H. Widyantoro, T. L. R. Mengko, and O. Kalsoem, “Inflammatory cell extraction and nuclei detection in pap smear images,” International Journal of E-Health and Medical Communications (IJEHMC), vol. 6, no. 2, pp. 27–43, 2015. doi: 10.4018/IJEHMC.2015040103
  7. H. Bandyopadhyay and M. Nasipuri, “Segmentation of pap smear images for cervical cancer detection,” in 2020 IEEE Calcutta Conference, Kolkata, India, Feb. 2020, pp. 30–33. doi: 10.1109/CALCON49167.2020.9106484
  8. D. Riana, H. Tohir, and A. N. Hidayanto, “Segmentation of overlapping areas on pap smear images with color features using K-means and otsu methods,” in Third International Conference on Informatics and Computing, Palembang, Indonesia, Oct. 2018, pp. 1–5. doi: 10.1109/IAC.2018.8780561
  9. N. Merlina, E. Noersasongko, P. Nurtantio, M. Soeleman, D. Riana, and S. Hadianti, “Detecting the width of pap smear cytoplasm image based on GLCM feature,” in Smart Trends in Computing and Communications: Proceedings of SmartCom 2020, pp. 231–240, 2020. doi: 10.1007/978-981-15-5224-3_22
  10. D. Kashyap et al., “Cervical cancer detection and classification using Independent Level sets and multi SVMs,” in 39th international conference on telecommunications and signal processing (TSP), Vienna, Austria, Jun. 2016, pp. 523–528. doi: 10.1109/TSP.2016.7760935
  11. M. Arya, N. Mittal, and G. Singh, “Texture-based feature extraction of smear images for the detection of cervical cancer,” IET Computer Vision , vol. 12, no. 8, pp. 1049–1059, 2018. doi: 10.1049/iet-cvi.2018.5349
  12. W. Wasswa, J. Obungoloch, A. H. Basaza-Ejiri, and A. Ware, “Automated segmentation of nucleus, cytoplasm and background of cervical cells from pap-smear images using a trainable pixel level classifier,” in IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, USA, Oct. 2019, pp. 1-9. doi: 10.1109/AIPR47015.2019.9174599
  13. D. Riana, D. H. Widyantoro, and T. L. Mengko, “Extraction and classification texture of inflammatory cells and nuclei in normal pap smear images,” in 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, Bandung, Indonesia, Nov. 2015, pp. 65–69. doi: 10.1109/ICICI-BME.2015.7401336
  14. A. Siswanto, A. Fadlil, and A. Yudhana, “Ekstraksi ciri metode gray level co-occurrence matrix untuk identifikasi sel darah putih,” JOINTECS (Journal of Information Technology and Computer Science), vol. 5, no. 2, pp. 71-88, 2020. doi: 10.31328/jointecs.v5i2.1334
  15. V. K. Mishra, S. Kumar, and N. Shukla, “Image acquisition and techniques to perform image acquisition,” SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, vol. 9, no. 1, 2017
  16. M. T. Sreedevi, B. S. Usha, and S. Sandya, “Papsmear image based detection of cervical cancer,” International Journal of Computer Applications, vol. 45, no. 20, pp. 35–40, 2012. doi:
  17. S. Hadianti and D. Riana, “Sistem pengenalan otomatis diameter citra mantoux untuk deteksi dini penyakit tbc kelenjar,” Jurnal Techno Nusa Mandiri, vol. 15, no. 2, pp. 77-83, 2018. doi: 10.33480/techno.v15i2.892
  18. M. Haralick, R. Robert, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics., vol. 3, no. 6, pp. 610–621, 1997. doi: 10.1109/TSMC.1973.4309314
  19. M. Yusa, E. Utami, and E. Luthfi, “Evaluasi performa algoritma klasifikasi decision tree id3,” INFOSYS (Information System) Journal, vol. 4, no. 1, pp. 23–34, 2016
  20. J. H. J. C. Ortega, M. R. Resureccion, L. R. Q. Natividad, E. T. Bantug, A. C. Lagman, and S. R. Lopez, “An analysis of classification of breast cancer dataset using J48 algorithm,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.3, pp. 475–480, 2020. doi: 10.30534/ijatcse/2020/7591.32020
  21. H. Seif, “Naive bayes and j48 classification algorithms on swahili tweets: perfomance evaluation,” International Journal of Computer Science and Information Security, vol. 14, no. 1, pp. 1-4, 2016
  22. I. A. Angreni, S. A. Adisasmita, M. I. Ramli, and S. Hamid, “Pengaruh nilai k pada metode k-nearest neighbor (KNN) terhadap tingkat akurasi identifikasi kerusakan jalan,” Rekayasa Sipil Mercu Buana, vol. 7, no. 2, pp. 63-70, 2019. doi: 10.22441/jrs.2018.v07.i2.01
  23. S. Fekri-Ershad, “Pap smear classification using combination of global significant value, texture statistical features and time series features,” Multimedia Tools and Applications., vol. 78, no. 22, pp. 31121–31136, 2019. doi: 10.1007/s11042-019-07937-y

Last update:

  1. RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data

    Zhu Tang, Yang Jiao, Mingmin Yuan. Systems and Soft Computing, 6 , 2024. doi: 10.1016/j.sasc.2024.200098

  2. E-commerce recommender system based on improved K-means commodity information management model

    Wei Zhang, Zonghua Wu. Heliyon, 10 (9), 2024. doi: 10.1016/j.heliyon.2024.e29045

Last update: 2024-11-20 01:03:21

No citation recorded.