Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging
Joint Authors
Valvano, Gabriele
Santini, Gianmarco
Martini, Nicola
Ripoli, Andrea
Iacconi, Chiara
Chiappino, Dante
Della Latta, Daniele
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-09
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Valvano, Gabriele& Santini, Gianmarco& Martini, Nicola& Ripoli, Andrea& Iacconi, Chiara& Chiappino, Dante…[et al.]. 2019. Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175456
Modern Language Association (MLA)
Valvano, Gabriele…[et al.]. Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging. Journal of Healthcare Engineering No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1175456
American Medical Association (AMA)
Valvano, Gabriele& Santini, Gianmarco& Martini, Nicola& Ripoli, Andrea& Iacconi, Chiara& Chiappino, Dante…[et al.]. Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175456
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175456