Abstract
We propose a method for discrimination and classification of mammograms with benign, malignant and normal tissues using independent component analysis and neural networks. The method was tested for a mammogram set from MIAS database, and multilayer perceptron neural networks, probabilistic neural networks and radial basis function neural networks. The best performance was obtained with probabilistic neural networks, resulting in 97.3% success rate, with 100% of specificity and 96% of sensitivity.
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INCa, Internet site address, in, http://www.inca.gov.br/acessed 04/05/2005
Bick, U., Giger, M., Schmidt, R., Nishikawa, R., Wolverton, D., Doi, K.: Computer- aided breast cancer detection in screening mammography. Digital Mammogr’9Chicago, IL, pp. 97–103 (1996)
Elmore, J.G., Wells, C.K., Lee, C.H., Howard, D.H., Feinstein, A.R.: Variability in radiologists’ interpretations of mammograms. New England Journal of Medicine 331(22), 1493–1499 (1994)
Bassett, L.W., Jackson, V.P., Jahan, R., Fu, Y.S., Gold, R.H.: Diagnosis of Diseases of the Breast. W. B. Saunders Company, Philadelphia (1997)
Kopans, D.B.: The positive predictive value of mammography. American Journal of Roentgenology 158(3), 521–526 (1993)
Tabar, L., Dean, P.B.: Teaching Atlas of Mammography, 2nd revised edn. Georg Thieme, New York (1985)
Petrick, N., Sahiner, B., Chan, H., Helvie, M.A., Paquerault, S., Hadjiiski, L.M.: Breast Cancer Detection: Evaluation of a Mass-Detection Algorithm for Computer-aided Diagnosis—Experience in 263 Patients. Radiology 224, 217–224 (2002)
Renato, C., Armando, B., et al.: A novel approach to mass detection in digital mammography based on Support Vector Machines (SVM). In: proceedings of the 6th International workshop in digital Mammography (IWDM), Bremem, Germany, pp. 399–401. Springer, Heidelberg (2002)
Christoyianni, I., Koutras, A., Kokkinakis, G.: Computer aided diagnosis of breast cancer in digitized mammograms. Comp. Med. Imag. & Graph. 26, 309–319 (2002)
Arons, B.: A review of cocktail party. MIT laboratory, Cambridge (1990)
Vigário, R.: Extraction of ocular artifacts form ecg using independent components analysis. Eletroenceph. Clin. Neurophysiol. 103(3), 395–404 (1997)
Vigário, R., Jousmaki, V., Hamalaien, M., Hari, R., Oja, E.: Independent component analysis for identification of artifacts in magnetoencephalographic recordings. In: Proc. NIPS 1997 andvances in neural information processing, vol. 10, pp. 229–235. MIT press, Cambridge (1998)
Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Indepent component analysis of electroencephalographic data. In: Advances in neural information processing systems, vol. 8, pp. 145–151. MIT press, Cambridge (1996)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9(7), 1483–1492 (1997)
Hyvärinen, A.: A family of fixed-point algorithms for independent component analysis. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1997), Munich, Germany, pp. 3917–3920 (1997)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. on Neural Networks (1999)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. WileyInterscience Publication, New York (1973)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1999)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Fast detection of masses in computer-aided mammography. IEEE Signal Process. Mag. 17(1), 54–64 (2000)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Neural classification of abnormal tissue in digital mammography using statistical features of the texture. In: IEEE Int Conf. Electron., Circuits Syst., vol. 1, pp. 117–120 (1999)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Suckling, J., et al.:: The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)
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Campos, L.F.A., Silva, A.C., Barros, A.K. (2005). Diagnosis of Breast Cancer in Digital Mammograms Using Independent Component Analysis and Neural Networks. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_48
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DOI: https://doi.org/10.1007/11578079_48
Publisher Name: Springer, Berlin, Heidelberg
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