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.
2017
9789811051210
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/894597
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