METODE SAMPLE BOOTSTRAPPING UNTUK MENINGKATKAN PERFORMA ALGORITMA NAIVE BAYES PADA CITRA TUNGGAL PAP SMEAR

Yumi Novita Dewi, Findi Ayu Sariasih

Abstract


ABSTRACT 

Research on cell classification of single pap smear images is an interesting thing to discuss, where the value of consent is very important to determine whether the cells are normal or not. An example of this study is to determine whether using the bootstraping sample method can improve the performance of the Bayes naive algorithm to classify single pap smear images that are on the herlev dataset. Approval values will be given for two classes and seven classes. The method used consists of several stages, namely preprocessing, knowledge rules, evaluation, and performance reports. The results of this study prove that the bootstrap sample method can increase the accuracy of seven classes to 85.24% and 93.24% for accuracy values with two classes.

Keywords: Sample Bootstrapping; Naive Bayes; Pap Smear.

 

ABSTRAK

  Penelitian mengenai klasifikasi sel citra tunggal pap smear menjadi hal yang menarik untuk dibahas, dimana nilai akurasi tersebut sangat penting untuk menetukan apakah sel-sel tersebut normal atau tidak. Penelitian ini bertujuan untuk menentukan apakah penggunaan metode sample bootstrapping dapat meningkatkan kinerja algoritma naive bayes untuk mengklasifikasikan citra tunggal pap smear yang ada pada dataset herlev. Nilai akurasi akan diperiksa untuk dua kelas dan tujuh kelas. Metode yang digunakan terdiri dari beberapa tahapan yaitu preprocessing, knowledge rule, evaluation, dan performance report. Hasil penelitian ini menunjukkan bahwa metode sample bootstrapping dapat meningkatkan nilai akurasi tujuh kelas menjadi 85,24% dan 93,24% untuk nilai akurasi dengan dua kelas.


Keywords


Sample Bootstrapping; Naive Bayes; Pap Smear.

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References


Baykut, A. and Ercil. A. “Towards Automated Classifier Combination for Pattern Recognition. Multiple Classifier Systems”, Springer Verlag, 2003, T. Wideatt, Fabio Roli (eds.), (2003) 94-105.

Bier., Fuzz., Sun, Q., & Chen, C., 2009, “A Comparison Study of Bayesian Classifier on Web Pages Classification, New Generation Computing”, 161-168.

Brian J. Burrows and Douglas L. Allaire. "A Comparison of Naive Bayes Classifiers with Applications to Self-Aware Aerospace Vehicles", 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum, (AIAA 2017-3819), https://doi.org/10.2514/6.2017-3819.

YN Dewi, D Riana, T Mantoro, Conference on Compututing, Engineering, and Design, “Improving Naive Bayes Performance in single Image Pap Smear Usig Weighted Principal Component Analysis (WPCA)”, 2017.

Champagne, Mcnairn, H., Daneshfar, B., & Shang, J. (2014). “A Bootstrap Method for Assessing Classification Accuracy and Confidence for Agricultural Land Use Mapping in Canada”. International Journal of Applied Earth Observations and Geoinformation, 29, 44–52. doi:10.1016/j.jag.2013.12.016.

Chen, X., & Samson, E. (2015). “Environmental Assessment of Trout Farming in France by Life Cycle Assessment : Using Bootstrapped Principal Component Analysis to Better Define System Classification”. Journal of Cleaner Production, 87, 87–95. doi:10.1016/j.jclepro.2014.09.021.

D. Riana, D. E. O. Dewi, D. H. Widyantoro, and T. L. R. Mengko, “Color canals modification with canny edge detection and morphological reconstruction for cell nucleus segmentation and area measurement in normal Pap smear images,” AIP Conf. Proc., vol. 1589, no. Icmns 2012, pp. 414–417, 2014.

D. Riana, M. E. Plissiti, C. Nikou, D. H. Widyantoro, and T. L. R. Mengko, “Inflammatory cell extraction and nuclei detection in Pap smear images,” Int. J. e-Health Med. Commun., vol. 6, no. 2, pp. 27–43, 2015.

D. Riana, D. H. Widyantoro, and T. L. Mengko, “Extraction and classification texture of inflammatory cells and nuclei in normal pap smear images,” Proc. - 2015 4th Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng. ICICI-BME 2015, pp. 65–69, 2016.

D. Riana, D. Ekashanti, O. Dewi, D. H. Widyantoro, and T. L. R. Mengko, “Segmentation and Area Measurement in Abnormal Pap Smear Images Using Color Canals Modification with Canny Edge Detection,” in International Conference on Women’s Health in Science & Engineering, 2012, pp. 1–4.

Dwiza, “Hierarchical Decision Approach Berdasarkan Importance Performance Analysis Untuk Klasifikasi Citra Tunggal Pap Smear Menggunakan Fitur Kuantitatif dan Kualitatif”. Depok: Fakultas Ilmu Komputer Program Magister Ilmu Komputer Universitas Indonesia, (2010).

D. Kashyap, A. Somani, and J. Shekhar, “Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs”, 39th Int. Conf. Telecommun. Signal Process., pp. 523–528, 2016.

E. Somers, “International Agency for Research on Cancer,” C. Can. Med. Assoc. J. J. l"Association medicale Can., vol. 133, no. 9, pp. 845–846, 1985.

E. Martin and J. Jantzen, “Pap-Smear Classification”. 2003.

Jantzen, J & Dounias, G., (2006), "Analysis of Pap Smear Image Data," Proceedings of the Nature-Inspired Smart Information Systems 2nd Annual Symposium NISIS.

Joaquín Abellán, Javier G. Castellano, "Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy", Entropy 2017, 19(6), 247; doi:10.3390/e19060247.

J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, “Pap-smear Benchmark Data For Pattern Classification”, Technical University of Denmark, Denmark, (2005).

J. Hyeon, C. Ho-Jin, B. D. Lee, and K. N. Lee, “Diagnosing Cervical Cell Images Using Pre-trained Convolutional Neural Network as Feature Extractor”, in Big Data and Smart Computing (BigComp), 2017 IEEE International Conference on, 2017, pp. 390–393.

J. Kittler, “Feature Selection & Extraction”, in Handbook of Pattern Recognition and Image Processing, Tzay Y. Young, King Sun Fu Ed. Academic Press, 1986.

Kale, A., & Aksoy, S. (2010), “Segmentation of Cervical Cell Images”, IEEE on International Conference Pattern Recognition (ICPR).

Kohavi, Ron (1995). "A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. 2 (12): 1137–1143.

Kohavi, Ron. "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection", Ijcai. Vol. 14. No. 2. 1995.

L. Zhang, L. Lu, I. Nogues, R. Summers, S. Liu, and J. Yao, “Deep Pap: Deep Convolutional Networks for Cervical Cell Classification”, IEEE J. Biomed. Heal. Informatics, vol. XX, no. c, pp. 1–1, 2017.

Martin, Erik. “Pap-Smear Classification”. Technical University of Denmark- DTU, (2003). http://fuzzy.iau.dtu.dk/download/martin2003.

Markus Hofmann, Ralf Klinkenberg, “RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)”,CRC Press, October 25, 2013.

Mc.Roberts, R. E., Magnussen, S., Tomppo, E. O., & Chirici, G. (2011). “Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data”. Remote Sensing of Environment, 115(12), 3165–3174.doi:10.1016/j. rse. 2011.07.002

M. S. A and S. Jereesh, "Automated Cervical Cancer Detection through RGVF segmentation and SVM Classification”, pdf. pp. 663–669, 2015.

Olive, D.J. (2017c), “Bootstrapping Hypothesis Tests and Confidence Regions,” unpublished manuscript with the bootstrap material from Olive (2017b) at (http://lagrange.math.siu.edu/Olive/ppvselboot.pdf).

Shepherd, J., Peersman, G., Weston, R., & Napuli, I. (2000), “Cervical cancer and sexual lifestyle : a systematic review of health education interventions targeted at women”, vol.15 (6), hal.681–694.

Shweta Kharya. "Using Data Mining Tecniques for Diagnosis and Prognosis of Cancer Disease". Chhatisgarh, India : Bhilai Institute of Technology. 2012.

Susanti, M.D.E., Tjandrasa, H., dan Fatichah, C. (2015), “Segmentasi Nukleus dan Sitoplasma pada Citra Smear Serviks menggunakan Kombinasi Metode Fuzzy C-Means Clustering dan Radiating Gradient Vector Flow Snake”, Tugas Akhir, Institut Teknologi Sepuluh Nopember, Surabaya.

Stone, Mervyn (1977). "Asymptotics for and against cross-validation". Biometrika. 64(1): 29–35.

Tian, W., Song, J., Li, Z., & Wilde, P. De. (2014). “Bootstrap Techniques for Sensitivity Analysis and Model Selection in Building Thermal Performance Analysis”. Applied Energy, 135,

Tri Agus Setiawan, Romi SW., & Abdul S."Integrasi Metode Sample Bootstrapping dan Weighted Principal Component Analysis untuk Meningkatkan Performa k Nearest Neighbor pada Dataset Besar", ISSN 2356-3982. Journal of Intelligent Systems, Vol. 1, No. 2, December 2015. 320–328. doi:10.1016/j.apenergy.2014.08.110.

WHO. "Human Papillomavirus (HPV) and Cervical Cancer", June 2016, http://www.who.int/mediacentre/factsheets/fs380/en/ (diakses pada tanggal 20 Agustus 2017, 15:30 wib).

Wu, H.S.,Gil, J.,& Barba, J. (1998), "Optimal segmentation of cell images”, IEEE Proceedings of Vision, Image and Signal Processing, vol.145(1), hal.50–56.

Y. Ramdani, D. Riana, and A. Mubarok, “Analisa Size Citra Sel Tunggal Nukleus Menggunakan Global Threshold dan Operasi Kanal Warna”. 2013.




DOI: https://doi.org/10.15408/jti.v12i1.11031 Abstract - 0 PDF - 0

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