A Convolutional Neural Networks Model for Breast Tissue Classification

Year 2022, Volume: 11 Issue: 3, 798 - 811, 30.09.2022

Mustafa Berkan Biçer , Hüseyin Yanık

https://doi.org/10.17798/bitlisfen.1102752

Abstract

Diagnosis of breast cancer and the determination of cancer type are essential information for cancer research in monitoring and managing the disease. Artificial intelligence techniques developed in recent years have led to many developments in medicine, as any information about the patient has become more valuable. Especially, artificial intelligence methods used in the detection and classification of cancer tissues directly assist physicians and contribute to the management of the treatment process. This study aims to classify breast tissues with ten different tissue characteristics utilizing the breast tissue data set, which has 106 electrical impedance spectroscopies taken from 64 patients in the UCI Machine Learning Repository database. Various machine learning algorithms such as k-nearest neighbors, support vector machine, decision tree, self-organizing fuzzy logic, and convolutional neural networks are used to classify these tissues with the accuracy of 81%, 78%, 82%, 92%, and 96%, respectively. This study demonstrated the benefit of the usage of convolutional neural networks in cancer detection and tissue classification. Compared to traditional methods, convolutional neural networks provided a more reliable and better results.

Keywords

breast tissue, classification, convolutional neural networks, machine learning, self-organizing fuzzy logic

References

• T. Holleczek, C. Zysset, B. Arnrich, D. Roggen, G. Troster G, “Towards an interactive snowboarding assistance system,” International Symposium on Wearable Computers; September 4-7, 2009, Linz, Australia: IEEE, 2009. pp. 147-148.
• T. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” IEEE Commun. Surv. Tutor., vol. 10, no.4, pp. 56-76, Fourth Quarter 2008.
• G. Cohen, S. Afshar, J. Tapson and A. van Schaik, "EMNIST: Extending MNIST to handwritten letters," 2017 International Joint Conference on Neural Networks (IJCNN), May 14-19, 2017, Anchorage: IEEE, 2017. pp. 2921-2926.
• A. L. Buczak and E. Guven, "A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection," IEEE Commun. Surv. Tutor., vol. 18, no. 2, pp. 1153-1176, Secondquarter 2016.
• A. Rajkomar, J. Dean, I. Kohane, “Machine learning in medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347-1358, April 2019.
• C. E. DeSantis, S. A. Fedewa, A. Goding Sauer, J.L. Kramer, R. A. Smith, A. Jemal, “Breast cancer statistics, 2015: Convergence of incidence rates between black and white women,” CA. Cancer J. Clin., vol. 66, no. 1, pp. 31-42, January 2016.
• J. Jossinet, M. Schmitt, “A review of parameters for the bioelectrical characterization of breast tissue,” Ann. NY. Acad. Sci., vol. 873, no. 1, pp. 30-41, February 2006.
• S. Gabriel, R. W. Lau, ve C. Gabriel, “The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues,” Physics in Medicine and Biology, vol. 41, no. 11, pp. 2271–2293, December 1996.
• W. Kubicek WG et al., “Impedance Cardiography as a Noninvasive Means to Monitor Cardiac Function,” JAAMI J. Ass. Advan. Med. Instrum., vol. 4, no. 2, pp. 79–84, 1970.
• J. Jossinet, “The impedivity of freshly excised human breast tissue,” Physiol. Meas., vol. 19, no. 1, pp. 61-75, February 1998.
• J. Estrela da Silva, J. P. Marques de Sá, and J. Jossinet, “Classification of breast tissue by electrical impedance spectroscopy,” Med. Biol. Eng. Comput., vol. 38, no. 1, pp. 26–30, January 2000.
• T. E. Kerner, K. D. Paulsen, A. Hartov, S. K. Soho and S. P. Poplack, "Electrical impedance spectroscopy of the breast: clinical imaging results in 26 subjects," IEEE Trans. Med. Imaging, vol. 21, no. 6, pp. 638-645, June 2002.
• D. Enachescu and C. Enachescu, "Learning Vector Quantization for Breast Cancer Prediction," Portuguese conference on artificial intelligence, December 5-8, 2005, Covilha, Portugal: IEEE, 2005, pp. 177-180.
• Y. Wu and S. C. Ng, "Combining Neural Learners with the Naive Bayes Fusion Rule for Breast Tissue Classification," 2nd IEEE Conference on Industrial Electronics and Applications, May 23-25, 2007, Harbin, China: IEEE, 2007, pp. 709-713
• D. K. Prasad, C. Quek and M. K. H. Leung, "A hybrid approach for breast tissue data classification," TENCON 2009 – 2009 IEEE Region 10 Conference, Jan 23-27, 2009, Singapore: IEEE, 2009, pp. 1-4.
• F. Calle-Alonso, C. J. Pérez, J. P. Arias-Nicolás, and J. Martín, “Computer-aided diagnosis system: A Bayesian hybrid classification method,” Comput. Meth. Prog. Bio., vol. 112, no. 1, pp. 104–113, 2013.
• M. R. Daliri, “Combining extreme learning machines using support vector machines for breast tissue classification,” Comput. Methods Biomech. Biomed. Eng., vol. 18, no. 2, pp. 185–191, 2015.
• K. Eroğlu, E. Mehmetoglu and N. Kılıç, "Success of ensemble algorithms in classification of electrical impadence spectroscopy breast tissue records," 22nd Signal Processing and Communications Applications Conference (SIU), April 23-25, 2014, Trabzon, Turkey: IEEE, 2014, pp. 1419-1422.
• C. Liu, T. Chang, and C. Li, “Breast Tissue Classification based on Electrical Impedance Spectroscopy,” Proceedings of the 2015 International Conference on Industrial Technology and Management Science, November 2015, Atlantis Press, 2015, pp. 237–240.
• Ayyappan GA. 2018. Novel Classification Approach-1 on Breast Tissue dataset.
• S. M. Rahman, M. A. Ali, O. Altwijri, M. Alqahtani, N. Ahmed, and N. U. Ahamed, “Ensemble-Based Machine Learning Algorithms for Classifying Breast Tissue Based on Electrical Impedance Spectroscopy,” Advances in Artificial Intelligence, Software and Systems Engineering, Cham, 2020, pp. 260–266.
• T. Sadad, A. Munir, T. Saba, and A. Hussain, “Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature,” J. Comput. Sci., vol. 29, pp. 34–45, 2018.
• Y.-D. Zhang, C. Pan, X. Chen, and F. Wang, “Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling,” J. Comput. Sci., vol. 27, pp. 57–68, 2018.
• J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Process. Lett., vol. 9, no. 3, pp. 293–300, Jun. 1999.
• T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21-27, January 1967.
• J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, Mar. 1986.
• X. Gu and P. P. Angelov, “Self-organising fuzzy logic classifier,” Inform. Sciences, vol. 447, pp. 36–51, 2018.
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, 2012, vol. 25. [Online]. Available: https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
• C. Szegedy et al., “Going Deeper With Convolutions,” Jun. 2015.
• K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv, 2014.