KLASIFIKASI MASSA PADA CITRA MAMMOGRAM MENGGUNAKAN KOMBINASI SELEKSI FITUR F-SCORE DAN LS-SVM
DOI:
https://doi.org/10.26594/teknologi.v6i1.558Abstract
ABSTRAK
Kanker payudara adalah penyakit yang paling umum diderita oleh perempuan pada banyak negara. Pemeriksaan kanker payudara dapat dilakukan menggunakan citra Mammogram dengan teknologi sistem Computer-Aided Detection (CAD). Analisis CAD yang telah dikembangkan adalah ekstraksi fitur GLCM, reduksi/seleksi fitur, dan SVM. Pada SVM (Support Vector Machine) maupun LS-SVM (Least Square Support Vector Machine) terdapat tiga masalah yang muncul, yaitu: Bagaimana memilih fungsi kernel, berapa jumlah fitur input yang dioptimalkan, dan bagaimana menentukan parameter kernel terbaik. Jumlah fitur dan nilai parameter kernel yang diperlukan saling mempengaruhi, sehingga seleksi fitur diperlukan dalam membangun sistem klasifikasi. Pada penelitian ini bertujuan untuk mengklasifikasi massa pada citra Mammogram berdasarkan dua kelas yaitu kelas kanker jinak dan kelas kanker ganas. Ekstraksi fitur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil proses ekstraksi fitur tersebut kemudian diseleksi mengunakan metode F-Score. F-Score diperoleh dengan menghitung nilai diskriminan data hasil ekstraksi fitur di antara data dua kelas pada data training. Nilai F-Score masing-masing fitur kemudian diurutkan secara descending. Hasil pengurutan tersebut digunakan untuk membuat kombinasi fitur. Kombinasi fitur tersebut digunakan sebagai input LS-SVM. Dari hasil uji coba penelitian ini didapatkan, bahwa menggunakan kombinasi seleksi fitur sangat berpengaruh terhadap tingkat akurasi. Akurasi terbaik didapat dengan menggunakan LS-SVM RBF dan SVM RBF baik dengan kombinasi seleksi fitur, maupun tanpa kombinasi seleksi fitur dengan nilai akurasi yaitu 97,5%. Selain itu juga seleksi fitur mampu mengurangi waktu komputasi.
Kata Kunci: F-Score, GLCM, kanker payudara, LS-SVM.
ABSTRACT
Breast cancer is the most common disease suffered by women in many countries. Breast cancer screening can be done using a mammogram image. Computer-aided detection system (CAD). CAD analysis that has been developed is GLCM efficient feature extraction, reduction / feature selection and SVM. In SVM (Support Vector Machine) and LS-SVM (Support Vector Machine Square least) there are three problems that arise, namely; how to choose the kernel function, how many input fea-tures are optimal, and how to determine the best kernel parameters. The number of fea-tures and value required kernel parameters affect each other, so that the selection of the features needed to build a system of classification. In this study aims to classify image of masses on digital mammography based on two classes benign cancer and malignant cancer. Feature extraction using gray level co-occurrence matrix (GLCM). The results of the feature extraction process then selected using the method F-Score. F-Score is obtained by calculating the value of the discriminant feature extraction results data between two classes of data in the data training. Value F-Score of each feature and then sorted in descending order. The sequenc-ing results are used to make the combination of fea-tures. The combination of these features are used as input LS-SVM. From the experiments that use a combination of feature selection affects the accuracy ting-kat. Best accuracy obtained using LS-SVM and SVM RBF RBF with combi-nation or without the combination of feature selection with accuracy value is 97.5%. It also features a selection able to curate the computa-tion time.
Keywords: Breast Cancer, F-Score, GLCM, LS-SVM.
References
Eurostat, "Healt statistic: atlas on mortaly in the European Union," Eurostat, Luxembourg, 2002.
H. C. Zuckerman, The role of mammography in the diagnosis of breast cancer. Breast cancer, diagnosis and treatment, New York: McGraw-Hill, 1987.
E. D. PISANO and F. SHTERN, "Image Processing And Computer Aided Diagnosis In Digital Mammography: A Clinical Perspective," International Journal of Pattern Recognition and Artificial Intelligence, vol. 7, no. 6, pp. 1493-1503, 1993.
S. Tai, Z. Chen and W. Tsai, "An Automatic Mass Detection System in Mammograms based on Complex Texture Features," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, pp. 618-627, 2014.
H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai and H. N. Du, "Approaches for automated detection and classification of masses in mammograms," Journal Pattern Recognition, vol. 39, no. 4, pp. 646-668, 2006.
F. Albregtsen, "Statistical Texture Measures Computed from Gray Level Coocurrence Matrices," University of Oslo, Oslo, 2008.
L. Yu and H. Liu, "Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution," in Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
Y.-W. Chen and C.-J. Lin, "Combining SVMs with Various Feature Selection Strategies," in Feature Extraction, vol. 207, Berlin Heidelberg, Springer, 2006, pp. 315-324.
M. F. Akay, "Support vector machines combined with feature selection for breast cancer diagnosis," Expert Systems with Applications , vol. 36, no. 2009, p. 3240–3247, 2009.
R. Aarthi, K. Divya, N. Komala and S. Kavitha, "Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine," in Third International Conference on Advanced Computing, Chennai, 2011.
J. A. K. Suykens and J. Vandewalle, "Least Squares Support Vector Machine Classifiers," Neural Processing Letters , vol. 9, no. 3, pp. 293-300, 1999.
S. Timp and N. Karssemeijer, "Interval change analysis to improve computer aided detection in mammography," Medical Image Analysis , vol. 10, no. 1, p. 82–95, 2006.
E. B. Holmes, G. L. White and D. K. Gaffney, "Ionizing Radiation Exposure, Medical Imaging," Medscape, 2010.
V. Vapnik, The nature of statistical learning theory, New York: Springer Science & Business Media, 2013.
L. Hakim, S. Mutrofin dan E. K. Ratnasari, “Segmentasi Citra menggunakan Support Vector Machine (SVM)dan Ellipsoid Region Search Strategy (ERSS) Arimoto Entropy berdasarkan Ciri Warna dan Tekstur,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 2, no. 1, pp. 11-16, 2016.
K. Pelckmans, J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, L. Lukas, B. Hamers, B. D. Moor and J.Vandewalle, "LS-SVMlab: a MATLAB/C toolbox for Least Squares Support Vector Machines," Leuven, Belgium, 2002.
K. Pelckmans, J. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor and J. Vandewalle, "LS-SVMlab toolbox user’s guide," Pattern recognition letters, vol. 24, no. 2003, pp. 659-675, 2003.
Downloads
Published
Issue
Section
License
Please find the rights and licenses in Teknologi: Jurnal Ilmiah Sistem Informasi. By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)' Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User/Public Rights
Register's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Register permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Register on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
Copyright and other proprietary rights relating to the article, such as patent rights,
The right to use the substance of the article in own future works, including lectures and books,
The right to reproduce the article for own purposes,
The right to self-archive the article (please read out deposit policy),
The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Register: Jurnal Ilmiah Teknologi Sistem Informasi).
5. Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Register will not be held liable for anything that may arise due to the author(s) internal dispute. Register will only communicate with the corresponding author.
6. Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Register entitles the author(s) to no royalties or other fees.
7. Miscellaneous
Register will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Register's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.