Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining a...

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Bibliographic Details
Published in:Journal of healthcare engineering 2019-01, Vol.2019 (2019), p.1-9
Main Authors: Della Latta, Daniele, Iacconi, Chiara, Ripoli, Andrea, Martini, Nicola, Santini, Gianmarco, Valvano, Gabriele, Chiappino, Dante
Format: Article
Language:English
Subjects:
Cable television broadcasting industry
Cancer
Detectors
Diagnosis
Mammography
Neural networks
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Summary:Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.
ISSN:2040-2295
2040-2309
DOI:10.1155/2019/9360941