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Breast cancer is an international public health concern. Medical imaging is one of the key elements in diagnosis. However, the quality of the interpretation of mammograms remains variable. One of the important characteristics in breast anatomy and physiology is breast tissue density. Density is important for two main reasons: first, increased breast density is associated with decreased mammographic sensitivity for the detection of breast cancer (Schetter, 2014). Second, breast density is one of the strongest known risk factors for breast cancer (Prevrhal et al., 2002; Boyd et al., 1995). For these reasons, automatic tissue density classification is an important process in diagnosis. Moreover, the BI-RADS (Breast Imaging-Reporting And Data System) classification system identifies four levels of breast density, but the mini-MIAS (Mammographic Image Analysis Society) database is divided into three density categories. In this article we describe a method for overall breast density classification using artificial neural networks. This approach has the advantages of not requiring a preprocessing step and the ability to be adapted to different mammography databases. The validation of our method is demonstrated using 240 mammograms from the DDSM database and 180 mammograms from mini-MIAS database, with the correct classification rate of 87.50% and 86.11%, respectively.
automatic classification, artificial neural networks, histogram.
Ahmad Z. et Roslin F. (2007). Modeling of real pH Neutralization Process using Multiple Neural Networks (MNN) Combination Technique. International Conference on Control, Instrumentation and Mechatronics Engineering 2007, Johor Bahru, Johor, Malaysia.
Barron A.R. (1993). Universal approximation bounds for superpositions of a sigmoidal function, IEEE Transactions on Information theory, vol. 39, no 3, p. 930-945.
Boyd N.F., Byng J.W., Jong R.A., Fishell E.K., Little L.E., Miller A.B. et Yaffe M.J. (1995). Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study, Journal of the National Cancer Institute, vol. 87, no 9, p. 670-675.
Boyd N.F., Guo H., Martin L.J., Sun L., Stone J., Fishell E. et Yaffe M.J. (2007). Mammographic density and the risk and detection of breast cancer, New England Journal of Medicine, vol. 356, no 3, p. 227-236.
Byng J.W., Boyd N.F., Fishell E., Jong R.A. et Yaffe M.J. (1994). The quantitative analysis of mammographic densities, Physics in Medicine and Biology, vol. 39, no 10, p. 1629.
Cherif M. (2010). Capacité d’une mémoire associative à fonction de sortie chaotique. Mémoire en informatique, Université du Québec à Montréal.
Cherif A. (2013). Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles. Thèse de doctorat. Université François-Rabelais de Tours.
Chen Z., Oliver A., Denton E. et Zwiggelaar R. (2013). Automated mammographic risk classification based on breast density estimation, Iberian Conference on Pattern Recognition and Image Analysis 2013, Madeira, Portugal.
Cybenko G. (1989). Approximation by superpositions of a sigmoidal function: Math. Control, Signals and Systems, vol. 2, no 4, p. 303-314.
Drew P.J. et Monson J.R. (2000). Artificial neural networks, Surgery, vol. 127, no 1, p. 3-11.
Dreyfus G., Martinez J.M., Samuelides M., Gordon M.B., Badran F., Thiria S. et Hérault L. (2002). Réseau de neurone méthodologie et application, Édition Eyrolles, Paris.
Heath M., Bowyer K., Kopans D., Kegelmeyer J.P., Moore R., Chang K. et Munishkumaran S. (1998). Current status of the digital database for screening mammography. In: Digital mammography, Springer Netherlands, p. 457-460.
Hornik K. (1993). Some new results on neural network approximation, Neural Networks, vol. 6, no 8, p. 1069-1072.
Jodouin J.F. (1994). Les Réseaux de neurones : principes et définitions, L’Hermes.
Kerlikowske K. (2007). The mammogram that cried Wolfe, New England Journal of Medicine,
vol. 356, no 3, p. 297-300.
Kolb T., Lichy J. et Newhouse J.H. (2002). Comparison of the performance of screening mammography, physical examination, and breast us and evaluation of factors that influence them: an analysis of 27,825 patient evaluations 1, Radiology, vol. 225, no 1, p. 165-175.
Liyakathunisa et Ravi Kumar C.N. (2011). A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction for Early Detection of Cancer in Low Resolution Mammogram Images, International Journal of Biometrics and Bioinformatics, vol. 5, no 2, p. 53-75.
McCormack V.A. et Dos Santos Silva I. (2006). Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis, Cancer Epidemiology Biomarkers & Prevention, vol. 15, no 6, p. 1159-1169.
Muhimmah I. et Zwiggelaar R. (2006). Mammographic density classification using multiresolution histogram information, Proceedings of the International Special Topic Conference on Information Technology in Biomedicine, ITAB 2006, Ioannina, Greece.
Mustra M., Grgi M. et Dela c V.K. (2012). Breast density classification using multiple feature selection, AUTOMATIKA, vol. 53, no 4, p. 362-372.
Nguyen H. et Chan C. (2004). Multiple neural networks for a long term time series forecast, Neural Computing & Applications, vol. 13, no 1, p. 90-98.
Oliveira J.E., Gueld M.O., Ara'ujo A.D.A., Ott B. et Deserno T.M. (2008). Toward a standard reference database for computer-aided mammography. In: Medical Imaging. International Society for Optics and Photonics. p. 69151Y-69151Y-9.
Oliveira J.E., Machado A.M., Chavez G.C., Lopes A.P.B., Deserno T.M. et Ara'ujo A.D.A. (2010). MammoSys: a content-based image retrieval system using breast density patterns, Computer Methods and Programs in Biomedicine, vol. 99, no 3, p. 289-297.
Oliver A., Freixenet J., Bosch A., Raba D. et Zwiggelaar R. (2005a). Automatic classification of breast tissue. In: Iberian Conference on Pattern Recognition and Image Analysis, p. 431-438.
Oliver A., Freixenet J., Bosch A. et Zwiggelaar R. (2005b). Automatic classification of breast density. In: IEEE International Conference on Image Processing, vol. 2, p. 1258-1261.
Patan K. (2008). Artificial neural networks for the modelling and fault diagnosis of technical processesSpringer Science & Business Media.
Petroudi S., Kadir T. et Brady M. (2003). Automatic classification of mammographic parenchymal patterns: A statistical approach. In: Engineering in Medicine and Biology Society. Proceedings of the 25th Annual International Conference of the IEEE. IEEE, 2003, Cancun, Mexico, p. 798-801.
Prevrhal S., Shepherd J.A., Smith-Bindman R., Cummings S.R. et Kerlikowske K. (2002). Accuracy of mammographic breast density analysis: results of formal operator training, Cancer Epidemiology Biomarkers & Prevention, vol. 11, no 11, p. 1389-1393.
Rivals I., Personnaz L., Dreyfus G. et Ploix J.L. (1995). Modélisation, classification et commande par réseaux de neurones : principes fondamentaux, méthodologie de conception et illustrations industrielles. Les réseaux de neurones pour la modélisation et la commande de procédés, JP Corriou ed. Lavoisier Tec & Doc.
Schetter S.E. (2014). Breast density as an independent risk factor for cancer, J Am Osteopath Coll Radiol, vol. 3, p. 10-19.
Seror J.Y. (2005). Quelles explorations chez une patiente à densité mammaire élevée ? Centre de Radiologie Duroc.
Stéphane T. (2012). Data Mining et statistique décisionnelle : l’intelligence des données, Éditions Technip.
Stines J. (2004). La densité mammaire : un concept radiologique, 26es journées de la SFSPM 2004, Nancy, p. 150-158.
Suckling J., Parker J., Dance D.R., Astley S., Hutt I., Boggis C. et Savage J. (1994). The Mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series 1994, p. 375-378.
Tardivon A. (2008). Quels risques, pour quelles femmes ? Densité mammaire et cancer du sein, 30es journées de la SFSPM 2008, La Baule, p. 93-101.
Tremblay G. (2004). Optimisation d’ensembles de classifieurs non paramétriques avec apprentissage par représentation partielle de l’information. Thèse de doctorat. École de technologie supérieure. Université du Québec.
Wolfe J.N. (1976a). Breast patterns as an index of risk for developing breast cancer, American Journal of Roentgenology, vol. 126, no 6, p. 1130-1137.
Wolfe J.N. (1976b). Risk for breast cancer development determined by mammographic parenchymal pattern, Cancer, vol. 37, no 5, p. 2486-2492.
Zribi M. et Boujelbene Y. (2012). Les réseaux de neurones un outil de sélection de variables : le cas des facteurs de risque de la maladie du cancer du sein, Éthique et économique/Ethics and Economics, vol. 9, no 1, p. 173-182.