Abstract
A computer-aided diagnosis platform has been developed in our laboratory for automatic detection of microcalcifications and opacities in mammograms. A complete description of the method has been given in previous articles [1]–[3]. In the present work, we report on the evaluation of results which were obtained for two mammography databases.
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References
J. Diahi, C. Frouge, A. Giron, and B. Fertil, “Artificial neural networks for detection of breast cancer in mammography,” in Digital mammography’ 96, K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt, Eds. Amsterdam: Elsevier, 1996, pp. 329–334.
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J. Diahi, PHD thesis, “Analyse de Mammographies par Techniques Neuromimétiques: Détection Automatiques des Microcalcifications et des Opacités Circonscrites,” Informatique Médicale department. Paris 5 University, 1997.
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© 1998 Springer Science+Business Media Dordrecht
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Diahi, J.G., Giron, A., Brahmi, D., Frouge, C., Fertil, B. (1998). Evaluation of a Neural Network Classifier for Detection of Microcalcifications and Opacities in Digital Mammograms. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_24
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DOI: https://doi.org/10.1007/978-94-011-5318-8_24
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