Classification of Masses in Digital Mammograms Using Firefly based Optimization

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Shankar Thawkar 1,* Ranjana Ingolikar 2

1. Department of Information Technology, Hindustan College of Science and Technology, Farah Mathura, 281122, India

2. Department of Computer Science, S. F. S. College Nagpur, 440001, India

* Corresponding author.


Received: 21 Aug. 2017 / Revised: 29 Sep. 2017 / Accepted: 17 Nov. 2017 / Published: 8 Feb. 2018

Index Terms

Firefly algorithm, artificial neural network, support vector machine, receiver operating characteristics curve, digital mammography, feature selection, classification


Breast cancer is one of the leading causes of death in women all over the world. Computer based diagnosis system assists radiologist in the effective treatment of breast cancer. To design an efficient classification system for masses in digital mammograms, we have to use efficient algorithms for feature selection to reduce the feature space of mammogram classification problem. The proposed study explores the use of Firefly algorithm to select a subset of features. Artificial neural network and support vector machine classifiers are employed to evaluate fitness of the selected features. Features selected by Firefly algorithm are used to classify masses into benign and malignant, using artificial neural network and support vector machine classifiers. The proposed method employed over 651 mammograms obtained from the Database of Digitized Screen-film Mammograms. Classification results show that Firefly algorithm with artificial neural network is superior to Firefly algorithm with support vector machine. Artificial neural network achieves accuracy of 95.23% with 94.43% sensitivity, 93.94% specificity and area under curve Az=0.965±0.008. On the other hand, support vector machine classifier achieves an accuracy of 92.47% with 96.14% sensitivity, 88.53% specificity and area under curve Az=0.951±0.009.Results obtained with Firefly algorithm shows that it will be useful for effective treatment of breast cancer.

Cite This Paper

Shankar Thawkar, Ranjana Ingolikar," Classification of Masses in Digital Mammograms Using Firefly based Optimization", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.2, pp. 25-33, 2018. DOI: 10.5815/ijigsp.2018.02.03


[1](NCI), N. C. I. Cancer stat fact sheets: Cancer of the breast. Available at: html / breast.html, May 2009.

[2]B. Acha, C. Serrano, R.M. Rangayyan and J.L. Desautels, “Detection of microcalcifications in mammograms,” Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. SPIE, Bellingham, 2006.

[3]C. Blum and Li. Xiaodong, "Swarm intelligence in optimization,” In Swarm Intelligence, pp. 43-85. Springer Berlin Heidelberg, 2008.

[4]D. Karaboga and B. Bahriye, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of global optimization, Vol.39, pp. 459-471, 2007.

[5]M. Dorigo and T. Stutzle, "The ant colony optimization metaheuristic: Algorithms, applications, and advances," International series in operations research and management science, pp. 251-286, 2003.

[6]J. Kennedy and R.C. Eberhart. "The particle swarm: social adaptation in information-processing systems," In New ideas in optimization, pp. 379-388. McGraw-Hill Ltd., UK, 1999.

[7]X. S. Yang, “Nature-inspired metaheuristic algorithms,” Luniver press, UK, pp. 242-246, 2008.

[8]X. S. Yang, "Firefly algorithms for multimodal optimization," In International symposium on stochastic algorithms, pp. 169-178, Springer, Berlin, Heidelberg, 2009.

[9]X. S. Yang and S. Deb, "Cuckoo search via Lévy flights," In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp. 210-214, IEEE, 2009.

[10]X. S. Yang, “A new metaheuristic bat-inspired algorithm,” Nature inspired cooperative strategies for optimization, pp. 65-74,2010.

[11]A. H. Gandomi and A. H. Alavi, "Krill herd: a new bio-inspired optimization algorithm," Communications in Nonlinear Science and Numerical Simulation, vol. 17, pp. 4831-4845, 2012.

[12]X. S. Yang and X. He, "Firefly algorithm: recent advances and applications," International Journal of Swarm Intelligence, vol. 1, pp. 36-50, 2013.

[13]E. Saraç and S. A. Özel, "Web page classification using firefly optimization," In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on, pp. 1-5, IEEE, 2013.

[14]J. Senthilnath, S. N. Omkar and V. Mani, "Clustering using firefly algorithm: performance study," Swarm and Evolutionary Computation, vol. 1, pp.164-171, 2011.

[15]H. Banati and M. Bajaj, "Firefly based feature selection approach," International Journal of Computer Science, vol.  8, pp. 473-480, 2011.

[16]C. L. Blake, "UCI repository of machine learning databases," html, 1998.

[17]S. K. Pal, C. S. Rai and A.  P. Singh. "Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems." International Journal of intelligent systems and applications, vol. 4, pp. 50, 2012.

[18]G. P. Singh and A. Singh, "Comparative study of Krill Herd, firefly and cuckoo search algorithms for unimodal and multimodal optimization." International Journal of Intelligent Systems and Applications, vol. 6, pp. 35, 2014.

[19]K. G. Dhal, I. Quraishi and S. Das, "A Chaotic Lmeta," International Journal of Image, Graphics and Signal Processing, vol. 7, pp. 69, 2015.

[20]S. Thawkar and R. Ingolikar, “Automatic Detection and Classification of Masses in Digital Mammograms," International Journal of Intelligent Engineering and Systems, vol. 10, pp. 65-74, 2017.

[21]S. Thawkar and R. Ingolikar, “Efficient approach for the classification of masses in digital mammograms," International journal of innovative computing information and control, vol.13, pp. 967-978,   2017.

[22]M. Sameti, R. K. Ward, J. Morgan-Parkes and B. Palcic, "A method for detection of malignant masses in digitized mammograms using a fuzzy segmentation algorithm," In Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, vol. 2, pp. 513-516,  IEEE, 1997.

[23]H. Li, Y. Wang, K. R. Liu, S. C.  Lo and M. T. Freedman, "Computerized radiographic mass detection. II. Decision support by featured database visualization and modular neural networks," IEEE transactions on medical imaging, vol. 20, pp. 302-313, 2001.

[24]K. Bovis, S. Singh, J. Fieldsend and C. Pinder, "Identification of masses in digital mammograms with MLP and RBF nets," In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 1, pp. 342-347, IEEE, 2000.

[25]S. Baeg, and N.  Kehtarnavaz, "Texture based classification of mass abnormalities in mammograms," In Computer-Based Medical Systems, 2000. CBMS 2000. Proceedings. 13th IEEE Symposium on, pp. 163-168, IEEE, 2000.

[26]R. P. Velthuizen and J. I. Gaviria, "Computerized mammographic lesion description," In [Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint, vol. 2, pp. 1034-vol, IEEE, 1999.

[27]D. B. Fogel, E. C. Wasson III, E. M. Boughton and V. W.  Porto, "Evolving artificial neural networks for screening features from mammograms," Artificial Intelligence in Medicine, vol. 14, pp. 317-326, 1998.

[28]C. E. Floyd, J. Y.  Lo, A. J. Yun, D. C. Sullivan and P. J. Kornguth," Prediction of breast cancer malignancy using an artificial neural network," Cancer, vol. 74, pp. 2944-2948, 1994.

[29]H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai and H. N. Du, "Approaches for automated detection and classification of masses in mammograms," Pattern recognition, vol. 39, pp. 646-668, 2006.

[30]M. W. Gardner and S. R. Dorling, "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences," Atmospheric environment, vol. 32, pp. 2627-2636, 1998.

[31]C. Cortes and V. Vapnik, “Support-vector networks," Machine learning, vol.  20, pp. 273-297, 1995.

[32]M. Heath, K.  Bowyer, D.  Kopans, R. Moore and W. P. Kegelmeyer, "The digital database for screening mammography," In Proceedings of the 5th international workshop on digital mammography, pp. 212-218, Medical Physics Publishing, 2000.

[33]M. Heath,  K.  Bowyer, D. Kopans, P. Kegelmeyer Jr, R. Moore, K. Chang and S. Munishkumaran, "Current status of the digital database for screening mammography," In Digital mammography, pp. 457-460, Springer Netherlands, 1998.

[34]J. A. Swets, "Measuring the accuracy of diagnostic systems," Science, vol. 240, pp. 1285-1293, 1988.

[35]M. Kubat, R. C. Holte and S. Matwin, "Machine learning for the detection of oil spills in satellite radar images," Machine learning, vol. 30, pp. 195-215, 1998.

[36]M. Kubat and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection," In ICML, vol. 97, pp. 179-186, 1997.