Abstract
One of the most serious and prominent cancers which affect more women in this world is breast cancer. Various kinds of breast cancer have been reported in medical literature that significantly differ in their capability to spread to other tissues in the human body. Plenty of risk factors have been traced through research though the exact reasons of breast cancer are not yet fully understood. Advances in medicine and technology help to improve the quality of life for breast cancer patients. A lot of contribution especially in the area of artificial intelligence and data mining is done to aid the diagnosis and classification of breast cancer. In order to provide assistance to the doctors, oncologists, and clinicians, neural networks and other optimization techniques serves as a great boon. Here, in this paper, firefly algorithm is utilized effectively as a powerful tool for the analysis and classification of breast cancer. Results show that an average classification accuracy of 98.52% is obtained when firefly algorithm is used for the classification of breast cancer.
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References
Polat K, Günes S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17:694–701
Setiono R (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 18:205–219
Übeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33:1054–1062
Mert A, Kılıç NZ, Bilgili E, Akan A (2015). Breast cancer detection with reduced feature set. Comput Math Methods Med 1–11
Rajaguru H, Prabhakar SK (2017) Bayesian linear discriminant analysis for breast cancer classification. In: 2nd IEEE international conference on communication and electronics systems. Coimbatore, India, pp 19–20 October 2017
Rajaguru H, Prabhakar SK (2016) A comprehensive analysis on breast cancer classification with radial basis function and gaussian mixture model In: 16th international conference on biomedical engineering (ICBME). Singapore, 7–10 December 2016
Dheeba J, Singh NA, Selvi ST (2014) Computer aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inf 49:45–52
Paulin F (2011) Classification of breast cancer by comparing backpropagation training algorithm. Int J Comput Sci Eng 3:327–332
Rajaguru H, Prabhakar SK (2017) Performance analysis of breast cancer classification with softmax discriminant classifier and linear discriminant analysis. In: International conference on biomedical and health informatics. Thessaloniki, Greece, pp 18–21 November 2017
Salama GI, Abdelhalim MB, Zeid MA (2012) Breast cancer diagnosis on three different data sets using multi-classifiers. Int J Comput Inf Technol 1:36–43
Pena-Reyes CA, Sipper M (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17:131–155
Rajaguru H, Prabhakar SK (2017) Expectation maximization based logistic regression for breast cancer classification In: IEEE proceedings of the international conference on electronics, communication and aerospace technology (ICECA 2017). Coimbatore, India, pp 603–606
Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Proceedings of the international symposium on stochastic algorithms, vol 5792. Springer, Berlin, pp 169–178
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Rajaguru, H., Prabhakar, S.K. (2019). A Study on Firefly Algorithm for Breast Cancer Classification. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_42
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DOI: https://doi.org/10.1007/978-3-030-00665-5_42
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