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

Public Health
Medicine

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