Classification of Breast Cancer Masses using Non-Linear Quadratic Support Vector Machine and Comparison with Self-Organizing Neural Network

Subject Areas : Artificial Intelligent

Soodeh Bakhshandeh ¹ (Department of Computer Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran)
Seyede Monire Atyabi ² (Department of Computer Engineering. South Tehran Branch Islamic Azad University. Tehran. Iran)
Sahar Saberi ³ (Department of Computer Engineering, East Tehran Branch, Islamic Azad University, Tehran)

Received: 2023-10-01 Accepted : 2023-12-05 Published : 2024-08-13

Keywords: Breast Cancer, Wisconsin Data, Support Vector Machine, Quadratic Kernel.,

Abstract :

Breast cancer is the second most common cancer after lung cancer and the fifth leading cause of death in women. In less developed countries, breast cancer is the most important cause of death. In this disease, the cells of the breast tissue change and divide into multiple cells and cause a lump. If breast cancer is in the early stages, treatment is possible. There are many treatment methods such as surgery to remove the defective area, drug therapy, radiation therapy, chemotherapy, hormone therapy, and immunotherapy. These treatments have the potential to save lives when administered in the early stages. From the above explanations, it can be seen that early detection of breast cancer is very important and in this research, an attempt has been made to identify suspected cancer data with the quadratic support vector machine method and based on the features extracted from valid and numerous MRI images. Let's classify so that the process of diagnosing the disease in the early stages is easier and faster. The results showed that 356 out of 357 malignant data and 202 out of 211 benign data were correctly classified. The classification accuracy of malignant data was 99.7% and the classification accuracy of benign data was 97.5%, and finally the overall classification accuracy was 98.2%, which indicates the optimal performance of this method in breast cancer data classification.

highlight:

Developing a method to diagnose breast cancer with the aim of diagnosing the disease in the early stages

Applying support vector machine with quadratic kernel with the aim of reducing classification time

Using the graphic feature selection method (SU-CFAM) with good speed and performance

Achieving 98.2%  accuracy without using SU-CFAM, and 99.1%  using it.

References:

[1] H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA Cancer J. Clin, vol. 71, no. 3, 2021, doi: 10.3322/caac.21660.
[2] H. Zerouaoui and A. Idri, “Reviewing machine learning and image processing based decision-making systems for breast cancer imaging,” Journal of Medical Systems, vol. 45, no. 8, pp. 1-20, 2021, doi: 10.1007/s10916-020-01689-1.
[3] S. Ekici and H. Jawzal “Breast cancer diagnosis using thermography and convolutional neural networks,” Medical hypotheses, vol. 137, p. 109542, 2020, doi: 10.1016/j.mehy.2019.109542.
[4] S. Sadhukhan, N. Upadhyay and P. Chakraborty, “Breast cancer diagnosis using image processing and machine learning,”. in Emerging Technology in Modelling and Graphics , vol. 937, pp. 113-127, 2020, doi: 10.1007/978-981-13-7403-6_12.
[5] P. Sahni and N. Mittal “Breast cancer detection using image processing techniques,” in Advances in interdisciplinary engineering, pp. 813-823, 2019, doi: 10.1007/978-981-13-6577-5_79.
[6] M. Adel, A. Kotb, O. Farag, M. S. Darweesh and H. Mostafa, "Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images," International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 2019, pp. 1-4, doi: 10.1109/MOCAST.2019.8741846.
[7] S. J. S. Gardezi, A. Elazab, B. Lei and T. Wang “Breast cancer detection and diagnosis using mammographic data: Systematic review,” Journal of medical Internet research, vol. 21, no. 7, p. e14464, 2019, doi: 10.2196/14464.
[8] I. Varlamis, I. Apostolakis, D. Sifaki-Pistolla, N. Dey, V. Georgoulias and C. Lionis “Application of data mining techniques and data analysis methods to measure cancer morbidity and mortality data in a regional cancer registry: The case of the island of Crete, Greece,” Computer methods and programs in biomedicine, vol. 145, pp. 73-83, 2017, doi: 10.1016/j.cmpb.2017.04.011.
[9] M. Tan, B. Zheng, J. K. Leader and D. Gur, "Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development," in IEEE Transactions on Medical Imaging, vol. 35, no. 7, pp. 1719-1728, July 2016, doi: 10.1109/TMI.2016.2527619.
[10] K. Yan et al., "Comprehensive autoencoder for prostate recognition on MR images," IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 2016, pp. 1190-1194, doi: 10.1109/ISBI.2016.7493479.
[11] M. Zhao, A. Wu, J. Song, X. Sun and N. Dong, “Automatic screening of cervical cells using block image processing,” Biomedical engineering online, vol. 15, no. 1, pp. 1-20, 2016, doi: 10.1186/s12938-016-0131-z.
[12] K. Sivakami and N. Saraswathi, “Mining big data: breast cancer prediction using DT-SVM hybrid model,” International Journal of Scientific Engineering and Applied Science (IJSEAS), vol. 1, no. 5, pp. 418-429, 2015.
[13] S. Bakhshandeh, R. Azmi and M. Teshnehlab, “Symmetric uncertainty class-feature association map for feature selection in microarray dataset,”. Int. J. Mach. Learn. and Cyber., vol. 11, pp. 15–32, 2020, doi: 10.1007/s13042-019-00932-7.
[14] A. Shmilovici, “Support Vector Machines,” in Data Mining and Knowledge Discovery Handbook, Springer, Boston, MA, 2005, pp. 257-276, doi: 10.1007/0-387-25465-X_12.
[15] https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic.
[16] Q. H. Nguyen et al., "Breast Cancer Prediction using Feature Selection and Ensemble Voting," International Conference on System Science and Engineering (ICSSE), Dong Hoi, Vietnam, 2019, pp. 250-254, doi: 10.1109/ICSSE.2019.8823106.
[17] A. H. Osman and H. M. A. Aljahdali, "An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model," in IEEE Access, vol. 8, pp. 39165-39174, 2020, doi: 10.1109/ACCESS.2020.2976149.
[18] D. Dumitru, “Prediction of recurrent events in breast cancer using the Naive Bayesian classification,” Annals of the University of Craiova, vol. 36, no. 2, 2009.
[19] D. Kaushik and K. Kaur, "Application of Data Mining for high accuracy prediction of breast tissue biopsy results," Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), Moscow, Russia, 2016, pp. 40-45, doi: 10.1109/DIPDMWC.2016.7529361.
[20] A. Mert, N. Kılıç, E. Bilgili and A. Akan, “Breast cancer detection with reduced feature set,” Computational and Mathematical Methods in Medicine, 2015, doi: 10.1155/2015/265138.
[21] W. K. Moon et al., “Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images,” in Ultrasound in medicineand biology, vol. 39, no. 7, pp. 1147-1157, 2013, doi: 10.1016/j.ultrasmedbio.2013.01.017.