Cluster of microcalcifications can be an early sign of breast cancer. In this paper we present a deep convolutional neural network for microcalcification detection and compare its results to a classical approach. In this work we used 238 mammograms to train and validate our neural network to recognize which pixels in a mammogram correspond to a calcification; we tested the results on 52 images and obtained an accuracy of 83.7% against only 58% of the classical approach. Our results show how deep learning could be an effective tool to use for microcalcification detection and segmentation, outdoing classical approaches.
Evaluation of a deep convolutional neural network method for the segmentation of breast microcalcifications in mammography imaging
Valvano, Gabriele
Primo
Methodology
;Martini, N.Membro del Collaboration Group
;Santini, G.Membro del Collaboration Group
;Gori, A.Membro del Collaboration Group
;Landini, L.Penultimo
Conceptualization
;Chiappino, D.Ultimo
Resources
2017-01-01
Abstract
Cluster of microcalcifications can be an early sign of breast cancer. In this paper we present a deep convolutional neural network for microcalcification detection and compare its results to a classical approach. In this work we used 238 mammograms to train and validate our neural network to recognize which pixels in a mammogram correspond to a calcification; we tested the results on 52 images and obtained an accuracy of 83.7% against only 58% of the classical approach. Our results show how deep learning could be an effective tool to use for microcalcification detection and segmentation, outdoing classical approaches.File in questo prodotto:
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