Abstract
The morbidity and mortality rate of breast cancer still continues to remain high among women across the world. This figure can be reduced if the cancer is identified at its early stage. A Computer-aided diagnosis (CAD) system is an efficient computerized tool used to analyze the mammograms for finding cancer in the breast and to reach a decision with maximum accuracy. The presented work aims at developing a CAD model which can classify the mammograms as normal or abnormal, and further, benign or malignant accurately. In the present model, CLAHE is used for image pre-processing, compound local binary pattern (CM-LBP) for feature extraction followed by principal component analysis (PCA) for feature reduction. Then, a chaotic whale optimization-based kernel extreme learning machine (CWO-KELM) is utilized to classify the mammograms as normal/abnormal and benign/malignant. The present model achieves the highest accuracy of 100% and 99.48% for MIAS and DDSM, respectively.
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References
Ahmed, F., Hossain, E., Bari, A., Hossen, M.S.: Compound local binary pattern (CLBP) for rotation invariant texture classification. Int. J. Comput. Appl. 33(6), 5–10 (2011)
Alatas, B.: Chaotic bee colony algorithms for global numerical optimization. Expert. Syst. Appl. 37(8), 5682–5687 (2010)
Bai, Z., Huang, G.B., Wang, D., Wang, H., Westover, M.B.: Sparse extreme learning machine for classification. IEEE Trans. Cybern. 44(10), 1858–1870 (2014)
Bajaj, V., Pawar, M., Meena, V.K., Kumar, M., Sengur, A., Guo, Y.: Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition. Neural Comput. Appl. 1–9 (2017)
Chithra Devi, M., Audithan, S.: Analysis of different types of entropy measures for breast cancer diagnosis using ensemble classification. Biomed. Res. 28(7), 3182–3186 (2017)
Christopher, M.B.: Pattern Recognition and Machine Learning. Springer, New York (2016)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dhahbi, S., Barhoumi, W., Kurek, J., Swiderski, B., Kruk, M., Zagrouba, E.: False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification. Comput. Meth. Prog. Biomed. 160, 75–83 (2018)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)
Han, M., Liu, B.: Ensemble of extreme learning machine for remote sensing image classification. Neurocomputing 149, 65–70 (2015)
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2000)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Jiao, Z., Gao, X., Wang, Y., Li, J.: A parasitic metric learning net for breast mass classification based on mammography. Pattern Recogn. 75, 292–301 (2018)
Junguo, H., Guomo, Z., Xiaojun, X.: Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data. Ecol. Model. 266, 86–96 (2013)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
World Health Organization: Burden: mortality, morbidity and risk factors. Global Status Report on Noncommunicable Diseases 2011 (2010)
Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., Muller, K.E.: Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the First Conference on Visualization in Biomedical Computing, pp. 337–345. IEEE (1990)
Raj, P., Muthulekshmi, M.: Review of cancer statistics in India. Int. J. Adv. Signal Image Sci. 1(1), 1–4 (2015)
Rampun, A., Scotney, B.W., Morrow, P.J., Wang, H., Winder, J.: Breast density classification using local quinary patterns with various neighbourhood topologies. J. Imaging 4(1), 14 (2018)
Reyad, Y.A., Berbar, M.A., Hussain, M.: Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. J. Med. Syst. 38(9), 100 (2014)
dos Santos Coelho, L., Mariani, V.C.: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst. Appl. 34(3), 1905–1913 (2008)
Silvestre, L.J., Lemos, A.P., Braga, J.P., Braga, A.P.: Dataset structure as prior information for parameter-free regularization of extreme learning machines. Neurocomputing 169, 288–294 (2015)
Singh, V.P., Srivastava, S., Srivastava, R.: Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol. Health Care 25(4), 709–727 (2017)
Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)
Thawkar, S., Ingolikar, R.: Classification of masses in digital mammograms using firefly based optimization. Int. J. Image Graph. Sig. Process. 10(2), 25 (2018)
Yang, D., Li, G., Cheng, G.: On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34(4), 1366–1375 (2007)
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Mohanty, F., Rup, S., Dash, B. (2018). An Improved CAD Framework for Digital Mammogram Classification Using Compound Local Binary Pattern and Chaotic Whale Optimization-Based Kernel Extreme Learning Machine. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_2
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