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BREAST CANCER SEVERITY PREDICATION USING DEEP LEARNING TECHNIQUES


(Received: 17-Sep-2019, Revised: 5-Nov-2019 , Accepted: 30-Nov-2019)
Breast cancer is one of the most common types of cancer most often affecting women. It is a leading cause of cancer death in less developed countries. Thus, it is important to characterize the severity of the disease as soon as possible. In this paper, we applied deep learning methods to determine the severity degree of patients with breast cancer, using real data. The aim of this research is to characterize the severity of the disorder in a shorter time compared to the traditional methods. Deep learning methods are used because of their ability to detect target class more accurately than other machine learning methods, especially in the healthcare domain. In our research, several experiments were conducted using three different deep learning methods, which are: Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Deep Boltzmann Machine (DBM). Then, we compared the performance of these methods with that of the traditional neural network method. We found that the f-measure of using the neural network was 74.52% compared to DNN which was 88.46 %, RNN which was 96.79% and DBM which was 97.28%.

[1] World Health Organization (WHO), "Cancer," [Online], Available: http://www.who.int/mediacentre/factsheets/fs297/en/, [Accessed on 12-9-2018].

[2] R. Oskouei, N. Kor and S. Maleki. "Data Mining and Medical World: Breast Cancer’s Diagnosis, Treatment, Prognosis and Challenges," American Journal of Cancer Research, vol. 7, no. 3, pp. 610- 627, Mar. 2017.

[3] Cleveland Clinic, "Breast Cancer," [Online], Available: https://my.clevelandclinic.org/health/diseases/ 3986-breast-cancer, [Accessed on 20-8-2018].

[4] Breastcancer.org, "Breast Cancer Stages,"[Online], Available: https://www.breastcancer.org/symptoms /diagnosis/staging,[Accessed on 26-10-2018].

[5] D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo and G.-Z. Yang, "Deep Learning for Health Informatics," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4–21, 2017.

[6] P. Danaee, R. Ghaeini and D. Hendrix. "A Deep Learning Approach for Cancer Detection and Relevant Gene Identification," Pacific Symposium on Biocomputing, vol. 2017, no. 22, pp. 219-229, 2017.

[7] J. Fombellida, S. Torres-Alegre and J. A. Piñuela. "Metaplasticity for Deep Learning: Application to WBCD Breast Cancer Database Classification," J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, F. J. Toledo-Moreo, H. Adeli (Eds.), "Bioinspired Computation in Artificial Systems," (IWINAC 2015), Lecture Notes in Computer Science, vol. 9108, Springer, Cham, 2015.

[8] W. Benzheng, H. Zhongyi, H. Xueying and Y. Y. Yin, "Deep Learning Model-based Breast Cancer Histopathological Image Classification," Proc. of the 2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, pp. 348-353, 2017.

[9] K. Sekaran, S. Ramalingam and C. Mouli, "Breast Cancer Classification Using Deep Neural Networks," S. Margret Anouncia and U. Wiil (Eds.), Knowledge Computing and Its Applications, Springer, Singapore, February 2018.

[10] M. Nawaz, A. Sewissy and T. Soliman, "Multi-class Breast Cancer Classification Using Deep Learning Convolutional Neural Network," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 9, no. 6, 2018.

[11] E. Rashed and A. Abou El Seoud, "Deep Learning Approach for Breast Cancer Diagnosis," Proceedings of the 8th International Conference on Software and Information Engineering, Cairo, Egypt, pp. 243-247, 09 – 12 April 2019.

[12] J. Xie, R. Liu, J. Luttrell and C. Zhang, "Deep Learning-based Analysis of Histopathological Images of Breast Cancer," Frontiers in Genetics, vol. 10, no. 80, 19 Feb. 2019.

[13] J. Schmidhuber, "Deep Learning in Neural Networks: An Overview," Neural Networks, vol. 61, pp. 85– 117, 2016.

[14] M. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.

[15] R. Collobert and J. Weston. "A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning," Proceedings of the 25th International Conference on Machine Learning (ICML '08), ACM, New York, NY, USA, pp. 160-167, 2008.

[16] T. Mikolov, M. Karafiát, L. Burget, J. ńĆernocký and S. Khudanpur. "Recurrent Neural Network-based Language Model," Proc. of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH-2010), pp. 1045-1048, 2010.

[17] D. Guota, "Fundamentals of Deep Learning–Introduction to Recurrent Neural Networks,"[Online], Available: https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/,[Accessed on 20-8-2018].

[18] R. Salakhutdinov and H. Larochelle, "Efficient Learning of Deep Boltzmann Machines," Journal of Machine Learning Research — Proceedings Track, vol. 2010, no. 9, pp. 693–700, 2010.