Abstract
Breast cancer is one of major diseases that cause high number of women’s death. To decrease these numbers, early diagnosis is an important task in medical process. Machine learning (ML) technics are an effective way to classify data especially in medical field, where those methods are widely used in diagnosis and decision making. In this paper, we present a review of the most recent publications that employ Machine Learning a pproaches in breast cancer diagnosis. The classification models discussed here are based on various ML techniques applied on different datasets.
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References
U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2008 Incidence and Mortality Web-based Report. Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute, Atlanta, GA (2012)
Sarah, M.: Cancers du sein et Immunologie anti-tumorale. Universite de Reims Champagne-ardenne, Ecole doctorale Sciendes Technologie Sante (547) (2014). Doctoral thesis
Vikas, C., Saurabh, P.: A novel approach for breast cancer detection using data mining techniques. Int. J. Innov. Res. Comput. Commun. Eng. 2(1), 2456–2465 (2014)
http://www.springer.com/lncs. Accessed 1 Feb 2019
Arpit, B., Aruna, T.: Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Appl. 42(10), 1–15 (2015)
Ashraf, O.I., Siti, M.S.: Intelligent breast cancer diagnosis based on enhanced Pareto optimal and multilayer perceptron neural network. Int. J. Comput. Aided Eng. Technol. Indersci. 10(5), 543–556 (2018)
Na, L., Qi, E., Xu, M., Bo, G., Gui-Qiu, L.: A novel intelligent classification model for breast cancer diagnosis. Inf. Process. Manag. 56, 609–623 (2019)
Nawel, Z., Nabiha, A., Nilanjan, D., Mokhtar, S.: Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. J. Med. Imaging Health Inf. 6, 53–62 (2016)
Abdulkader, H., John, B.I., Rahib, H.A.: Machine learning techniques for classification of breast tissue. In: 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW, pp. 402–410. Proc. Comput. Sci. Elsevier, Budapest (2017)
Haifeng, W., Bichen, Z., Sang, W.Y., Hoo, S.K.: A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur. J. Oper. Res. 267(2), 1–33 (2017)
Kemal, P., Ümit, Ş.: A novel ML approach to prediction of breast cancer: combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier’. In: 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–4. IEEE (2018)
Teresa, A., Guilherme, A., Eduardo, C., José, R., Paulo, A., Catarina, E., António, P., Aurélio, C.: Classification of breast cancer histology images using convolutional neural networks. PLoS One 12(6), 1–14 (2017)
Fabio, A.S., Luiz, S.O., Caroline, P., Laurent, H.: Breast cancer histopathological image classification using convolutional neural networks. In: Conference: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2016)
Hiba, A., Hajar, M., Hassan, A.M., Thomas, N.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. In: The 6th International Symposium on Frontiers in Ambient and Mobile Systems (FAMS), Proc. Comput. Sci., pp. 1064–1069. Elsevier (2016)
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Houfani, D., Slatnia, S., Kazar, O., Zerhouni, N., Merizig, A., Saouli, H. (2020). Machine Learning Techniques for Breast Cancer Diagnosis: Literature Review. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1103. Springer, Cham. https://doi.org/10.1007/978-3-030-36664-3_28
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DOI: https://doi.org/10.1007/978-3-030-36664-3_28
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