Residual Convolutional Neural Networks for Breast Density Classification

Francesca Lizzi, Stefano Atzori, Giacomo Aringhieri, Paolo Bosco, Carolina Marini, Alessandra Retico, Antonio Traino, Davide Caramella, M. Fantacci

Abstract

In this paper, we propose a data-driven method to classify mammograms according to breast density in standard. About 2000 mammographic exams have been collected from the “Azienda Pisana” (AOUP, Pisa, IT). The dataset has been classified according to breast density in the BI-RADS standard. Once the dataset has been labeled by a radiologist, we proceeded by building a Residual Neural Network in order to classify breast density in two ways. First, we classified mammograms using two “super-classes” that are dense and non-dense breast. Second, we trained the residual neural network to classify mammograms according to the four classes of the BI-RADS standard. We evaluated the performance in terms of the accuracy and we obtained very good results compared to other works on similar classification tasks. In the near future, we are going to improve the results by increasing the computing power, by improving the quality of the ground truth and by increasing the number of samples in the dataset.

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Paper Citation


in Harvard Style

Lizzi F., Atzori S., Aringhieri G., Bosco P., Marini C., Retico A., Traino A., Caramella D. and Fantacci M. (2019). Residual Convolutional Neural Networks for Breast Density Classification.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-353-7, pages 258-263. DOI: 10.5220/0007522202580263


in Bibtex Style

@conference{bioinformatics19,
author={Francesca Lizzi and Stefano Atzori and Giacomo Aringhieri and Paolo Bosco and Carolina Marini and Alessandra Retico and Antonio Traino and Davide Caramella and M. Fantacci},
title={Residual Convolutional Neural Networks for Breast Density Classification},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={258-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007522202580263},
isbn={978-989-758-353-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Residual Convolutional Neural Networks for Breast Density Classification
SN - 978-989-758-353-7
AU - Lizzi F.
AU - Atzori S.
AU - Aringhieri G.
AU - Bosco P.
AU - Marini C.
AU - Retico A.
AU - Traino A.
AU - Caramella D.
AU - Fantacci M.
PY - 2019
SP - 258
EP - 263
DO - 10.5220/0007522202580263