Abstract
Computer-aided diagnosis (CAD) of breast cancer is becoming a necessity given the exponential growth of performed. CAD are usually characterized by the large volume of acquired data that must be labeled in a specific way that leads to a major problem which is labeling operation. As a result the community of machine learning has attempted to respond to these practical needs by introducing the semi-supervised learning. The motivation of the current research is to propose a TSVM-CAD system for mammography abnormalities detection using a new Transductive TSVM with comparison of its kernel functions. The effectiveness of the system is examined on the Digital Database for Screening Mammography database DDSM using classification accuracy, sensitivity and specificity. Experimental results are very encouraging.
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References
Cedolini, C., Bertozzi, S., Londero, A., Bernardi, S., Seriau, L., Concina, S.: Type of breast cancer diagnosis, screening, and survival. Clin. Breast Cancer 14, 235–240 (2014)
Zheng, B., Yoon, S., Lam, S.: Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41, 1476–1482 (2014)
Abbadi, N.E., Taee, E.A.: Breast cancer diagnosis by CAD. Int. J. Comput. Appl. 100, 25–29 (2014)
Côme, E., Oukhellou, L., Denoeux, T., Aknin, P.: Learning from partially supervised data using mixture models and belief functions. Pattern Recogn. 42, 334–348 (2009)
Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Filipovych, R., Davatzikos, C.: Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55, 1109–1119 (2011)
Pang, S., Ban, T., Kadobayashi, Y., Kasabov, N.: Personalized mode transductive spanning SVM classification tree. Inf. Sci. 181, 2071–2085 (2011)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1996)
Grishagin, I.V.: Automatic cell counting with ImageJ. Anal. Biochem. (In Press)
Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E.: A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification. Expert Syst. Appl. 41, 7457–7467 (2013)
Žunić, D., Žunić, J.: Shape ellipticity from Hu moment invariants. Appl. Math. Comput. 226, 406–414 (2014)
Grubbström, D., Tang, O.: The moments and central moments of a compound distribution. Eur. J. Oper. Res. 170, 106–119 (2006)
Azizi, N., Zemmal, N., Tlili, Y.: Kernel Based classifiers fusion with features diversity for breast masses classification. In: Proceedings of the 8th International Workshop on Systems, Signal Processing and Their Application (WoSSPA), pp. 116–121 (2013)
Heath, M.D., Bowyer, K.W.: Mass detection by relative image intensity. In: 5th International Workshop on Digital Mammography, Toronto, Canada, pp. 219–225 (2000)
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Zemmal, N., Azizi, N., Sellami, M., Dey, N. (2016). Automated Classification of Mammographic Abnormalities Using Transductive Semi Supervised Learning Algorithm. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-319-30298-0_73
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DOI: https://doi.org/10.1007/978-3-319-30298-0_73
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