Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

Lihat/Buka File Repository

Lihat/Buka File Peer Review

Tanggal

2018-12-12

Abstraksi

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification. 

Kata Kunci: Cervical cancer Genetic algorithm Hierarchical Decision Approach (HAD) Neural Network (NN) Pap smear

Bidang ilmu
Image Processing

Bibliografi

[1] "World's health ministers renew commitment to cancer prevention and control.," May 2017. [Online]. Available: http://www.who.int/cancer/media/news/cancerprevention-resolution/en/.

[2] D. Riana, W. H. Dwi, D. E. Dewi and T. L. R. Mengko, "Segmentasi Luas Nukleus Sel Normal Superfisial Pap Smear Menggunakan Operasi Kanal Warna Dan Deteksi Tepi," Seminar Nasional Inovasi dan Teknologi (SNIT), 2012.

[3] J. Jantzen, J. Norup, G. Dounias and B. Bjerregaard, "Pap-smear Benchmark Data For Pattern Classification," 2005.

[4] E. Martin, "Pap-Smear Classification," Technical University of Denmark - DTU, 2003.

[5] Pruengkarn, Ratchakoon, Kok Wai Wong and Chun Che Fung, "A review of data mining techniques and applications," Journal of Advanced Computational Intelligence and Intelligent Informatics 21, vol. 1, pp. 31-48, 2017.

[6] D. Riana, D. H. Widyantoro and T. L. Mengko, "Extraction and classification texture of inflammatory cells and nuclei in normal Pap smear images," in ICI-BME, Bandung, 2015.

[7] 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," in AIP, Bandung, 2014.

[8] D. Riana, 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, pp. 27-43, 2015.

[9] D. Riana, D. E. 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 In International Conference on Women’s Health in Science & Engineering, Bandung, 2012.

[10] R. Kurniawan, A. Kurniawardhani and I. Muhimmah, "Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach," TELKOMNIKA Telecommunication, Computing, Electronics and Control, Vol 16 (5): 2048-2056; 2018.

[11] J. Hyeon, H.-J. Choi and B. D. Lee, "Diagnosing Cervical Cell Images Using Pre-trained Convolutional Neural Network as Feature Extractor," in in Big Data and Smart Computing (BigComp), 2017.

[12] D. Kashyap, A. Somani and J. Shekhar, "Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs," in 9th Int. Conf. Telecommun. Signal Process, 2016.

[13] N. Lassouaoui, L. Hamami and N. Nouali, "Morphological description of cervical cell images for the pathological recognition," Int. J. Med. Health, Vols. 1, No 5, pp. 313-316, 2007.

[14] D. Riana, "Hierarchical Decision Approach Berdasarkan Importance Performance Analysis Untuk Klasifikas Citra Tunggal Pap Smear Menggunakan Fitur Kuantitatif dan Kualitatif," 2010. [Online]. Available: http://www.digilib.ui.ac.id/detail?id=31702&lokasi=12. [Accessed 20 March 2018].

[15] T. K. Mansoori, A. Suman and S. K. Mishra, "Feature Selection by Genetic Algorithm and SVM Classification for Cancer Detection," International Journal of Advanced Research in Computer Science and Software Engineering Vol 4, pp. 357-365, 2014.

[16] M. H. A. Yazid, S. Talib, M. H. Satria and A. A. Ghazi, "Neural Network on Mortality Prediction for the Patient Admitted with ADHF (Acute Decompensated Heart Failure)," in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics, Yogyakarta, 2017.

[17] M. F. Mohammed and T. H. Rassem, "An Ensemble of Enhanced Fuzzy Min Max Neural Networks for Data Classification," TELKOMNIKA Telecommunication, Computing, Electronics and Control, Vol 15 (2); 2017.

[18] A. G. Karegowda, A. Manjunath and M. Jayaram, "Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis Of Pima Indians Diabetes," International Journal on Soft Computing (IJSC), pp. 15-23, 2011.

[19] Y. Ramdhani and D. Riana, "Hierarchical Decision Approach Based on Neural Network and Genetic Algorithm Method for Single Image Classification of Pap Smear," in Informatic and Computing (ICIC), Jayapura, 2017.

[20] M. Orozco-Monteagudo, C. Mihai, H. Sahli and A. Taboada-Crispi, "Combined Hierarchical Watershed Segmentation and SVM Classification for Pap Smear Cell Nucleus Extraction," 2012.

[21] E. J. Mariarputham and A. Stephen, "Nominated Texture Based Cervical Cancer Classification," Computational and Mathematical Methods in Medicine, pp. 1-10, 2015.

[22] A. Sarwar, V. Sharma and R. Gupta, "Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis," Personalized Medicine Universe, pp. 1-9, 2015.

[23] Y. Ramdhani, "Komparasi Algoritma LDA DAN Naive Bayes Dengan Optimasi Fitur Untuk Klasifikasi Citra Tunggal Pap Smear," Informatika, Vols. III, No, 2, pp. 434-441, 2015.

[24] D. Riana, A. N. Hidayanto and Fitriyani, "Integration of Bagging and greedy forward selection on image pap smear classification using Naïve Bayes," in Cyber and IT Service Management (CITSM), Bali, 2017.