A new method for detection of breast cancer in mammography images using a firefly algorithm
Subject Areas : Renewable energy
Ghazal Mardanian
1
(
MSc - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Neda Behzadfar
2
(
Assistant Professor - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Keywords: Firefly Algorithm, breast cancer, Morphology, digital mammography images,
Abstract :
Breast cancer is one of the most common cancers among women. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. In recent years, artificial intelligence systems have been the focus of much attention in preventing the subjective analysis of mammograms and physicians by radiologists and enhancing the accuracy of breast cancer detection. In this study, combining the firefly algorithm and applying appropriate image processing to detect breast cancer in mammographic images has been investigated. In this paper, mammographic images in the DDSM dataset were used. Three performance metrics such as sensitivity, specificity and accuracy (93.4%, 91%, 95%) were used to analyze the detection performance. The proposed work shows better performance when compared to existing work in literature.
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[29] A. Keshavarz, H. Pourghasem, S. M. S. Ebrahimi, "Segmentation of melanoma and other pigmented skin lesions in dermoscopic images using fusion of threshoding methods based on reinforcement algorithm", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 16, pp. 37-48, Winter 2014.
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_||_[1] B. Milovic, "Prediction and decision making in health care using data mining", Kuwait Chapter of Arabian Journal of Business and Management Review, Vol. 1, No, 12, pp. 126-136, 2012.
[2] M. H. M. Adnan, W. Husain, N. A. Rashid, "Data mining for medical systems: A review." Proceedings International Conference on Advances in Computer and Information Technology-ACIT. 2012 (doi: 10.3850/978-981-07-3161-8_ACIT-170).
[3] V. Rafe, R. H. Farhoud, "A survey on data mining approaches in medicine", International Research Journal of Applied and Basic Science, Vol. 4, No. 1, pp. 196-202, 2013.
[4] Y. Cho, C. Chin, K. Wang, "Based on fuzzy linear discriminant analysis for breast cancer mammography analysis", Proceeding of the IEEE/TAAI, Chung-Li, Taiwan, Noc. 2011 (doi:10.1109/TAAI.2011.18).
[5] D. A. Schauer, W. L. Otha, "National council on radiation protection and measurements report shows substantial medical exposure increase", Radiology, Vol. 253, No. 2, pp. 293-296, 2009 (doi:10.1148/radiol.2532090494).
[6] J. Ferlay, S. HR, F. Bray, D. Forman, C. Mathers, D, M. Parkin, "Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008", International Journal of Cancer, Vol. 127, No. 12, pp. 2893-2917, Dec. 2010 (doi:10.1002/ijc. 25516.).
[7] H. Mahmoodian, S. Nasr, "Insulin drug regulation by general type 2 fuzzy controller with alpha plane", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 37, pp. 39-48, Spring 2019.
[8] F. Hourali, M. Momeny, S. Gharrav, "Reducing the impact of SYN flood attacks by improving the accuracy of the PSO algorithm by adaptive effective filters", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 37, pp. 51-57, Spring 2019.
[9] S. Runjie, Y. Yang, and F. Shao. "Intelligent breast cancer prediction model using data mining techniques." Proceedings of the IEEE sixth international conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, pp. 384-387. 2014 (doi: 10.1109/IHMSC.2014.100).
[10] P. S. Pawar, and D. R. Patil. "Breast cancer detection using neural network models", Proceedings of the IEEE/ Communication Systems and Network Technologies , Gwalior, India, pp. 568-572.2013 (doi: 10.1109/CSNT. 2013.122).
[11] M. Sadeghzadeh, "A New Method for Diagnosing Breast Cancer using Firefly Algorithm and Fuzzy Rule based Classification", Proceedings of the IEEE/AICT , Moscow, Russia, pp. 1-5, 2017 (doi: 10.1109/ICAICT. 2017.8687061).
[12] S. Ghosh, S. Mondal, B. Ghosh, "A comparative study of breast cancer detection based on SVM and MLP BPN classifier", Proceedings of the IEEE/ACES, pp. 1-4, Hooghy, India, 2014 (doi: 10.1109/ACES.2014.6808002).
[13] M. R. Yousefi, "Magnetic induction tomography: a review of process and medical tomography systems", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 31, pp. 33-50, Autumn 2017.
[14] G. Shahgholian, A. Movahedi, "Modeling and controller design using ANFIS method for non-linear liquid level system", International Journal of Information and Electronics Engineering, Vol. 1, No. 3, pp. 271-275, Nov. 2011 (doi:10.7763/IJIEE.2011.V1.43).
[15] N. Behzadfar, S. M. P. Firoozabadi, K. Badie, "Low-complexity discriminative feature selection from eeg before and after short-term memory task", Clinical EEG and neuroscience, Vol. 47, No. 4, pp. 291-297, Feb. 2016 (doi:10.1177/1550059416633951).
[16] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, Vol. 39, No. 3, pp. 459-471, Nov. 2007 (doi: 10.1007/s 10898-007-9149-x).
[17] G. Shahgholian, S. Fazeli-Nejad, M. Moazzami, M. Mahdavian, M. Azadeh, M. Janghorbani, S. Farazpey, "Power system oscillations damping by optimal coordinated design between PSS and STATCOM using PSO and ABC algorithms", Proceeding of the IEEE/ECTICON, Chiang Mai, Thailand, pp. 1-6, June/July 2016 (doi:10.1109/ECTICon.2016.7561458).
[18] M. Dorigo, T. Stützle, "The ant colony optimization metaheuristic: Algorithms, applications, and advances", Handbook of Metaheuristics, Springer, Boston, MA, pp. 250-285, 2003 (doi:10.1007/0-306-48056-5_9).
[19] M. Lotfi-Forushani, B. Karimi, G. Shahgholian, "Optimal PID controller tuning for multivariable aircraft longitudinal autopilot based on particle swarm optimization algorithm", Journal of Intelligent Procedures in Electrical Technology, Vol. 3, No. 9, pp. 41-50, Spring 2012.
[20] X. S. Yang, "A new metaheuristic bat-inspired algorithm", Nature Inspired Cooperative Strategies for Optimization, Springer, Berlin, Heidelberg, pp. 65-74, 2010 (doi:10.1007/978-3-642-12538-6_6).
[21] A. H. Gandomi, A. H. Alavi, "Krill herd: A new bio-inspired optimization algorithm", Communications in Nonlinear Science and Numerical Simulation, Vol. 17, No. 12, pp. 4831-4845, Dec. 2012 (doi:10.1016/j.cnsns. 2012.05.010).
[22] X. S. Yang, S. Deb, "Cuckoo search via Lévy flights". Proceedings of the IEEE/NaBIC, pp. 210-214, Coimbatore, India. 2009 (doi: 10.1109/NABIC.2009.5393690).
[23] X. S.Yang, "Firefly algorithms for multimodal optimization", International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, Vol. 5792, pp. 169-178, 2009 (doi:10.1007/978-3-642-04944-6_14).
[24] S. Thawkar, R. Ingolikar, "Classification of masses in digital mammograms using firefly based optimization", International Journal of Image, Graphics & Signal Processing, Vol. 10, No. 2, Feb. 2018 (doi: 10.5815/ijigsp .2018.02.03).
[25] X. S. Yang, "Firefly algorithm, stochastic test functions and design optimisation", International Journal of Bio-Inspired Computation, Vol. 2, No. 2 , pp. 78-84, Mar. 2010.
[26] W. B. Sampaio, A. C. Silva, A. C. Paiva, M. Gattass, "Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM", Expert Systems with Applications, Vol. 42, No. 22, pp. 8911-8928, Dec. 2015 (doi:10.1016/j.eswa.2015.07.046).
[27] S. W. Borges, E.M. Diniz, A. C. Silva, A. C. Paiva, M. Gattass, "Detection of masses in mammogram images using CNN, geostatistic functions and SVM", Computers in Biology and Medicine Vol.41, No.8, pp. 653-664, 2011 (doi:10.1016/j.compbiomed.2011.05.017).
[28] A. P. Nunes, , A. C. Silva, A. C. Paiva, "Detection of masses in mammographic images using simpson’s diversity index in circular regions and svm", International Journal of Signal and Imaging Systems Engineering, Vol. 3, No. 1, pp. 40-51, 2010 (doi:10.1007/978-3-642-03070-3_41).
[29] A. Keshavarz, H. Pourghasem, S. M. S. Ebrahimi, "Segmentation of melanoma and other pigmented skin lesions in dermoscopic images using fusion of threshoding methods based on reinforcement algorithm", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 16, pp. 37-48, Winter 2014.
[30] H. R. Javadi , H. Pourghasem, "kin lesion classification from dermoscopy images using color and shape features", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 29, pp. 33-40, Spring 2017.
