Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Lourdes Duran-Lopez; Juan Pedro Dominguez-Morales; Isabel Amaya-Rodriguez; Francisco Luna-Perejon; Javier Civit-Masot; Saturnino Vicente-Diaz; Alejandro Linares-Barranco

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Paper

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks; Deep Learning

In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, 444-448, 2019 , Vienna, Austria

Authors: Lourdes Duran-Lopez ; Juan Pedro Dominguez-Morales ; Isabel Amaya-Rodriguez ; Francisco Luna-Perejon ; Javier Civit-Masot ; Saturnino Vicente-Diaz and Alejandro Linares-Barranco

Affiliation: Robotics and Technology of Computers Lab., University of Seville, Seville 41012 and Spain

Keyword(s): Breast Cancer, Mammography, Deep Learning, Convolutional Neural Network, Faster Regional Convolutional Neural Network, Medical Image Analysis.

Abstract: Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign . The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images. (More)

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Paper citation in several formats:

Duran-Lopez, L., Dominguez-Morales, J. P., Amaya-Rodriguez, I., Luna-Perejon, F., Civit-Masot, J., Vicente-Diaz, S. and Linares-Barranco, A. (2019). Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA; ISBN 978-989-758-384-1; ISSN 2184-3236, SciTePress, pages 444-448. DOI: 10.5220/0008494304440448

@conference{ncta19,
author={Lourdes Duran{-}Lopez and Juan Pedro Dominguez{-}Morales and Isabel Amaya{-}Rodriguez and Francisco Luna{-}Perejon and Javier Civit{-}Masot and Saturnino Vicente{-}Diaz and Alejandro Linares{-}Barranco},
title={Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA},
year={2019},
pages={444-448},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008494304440448},
isbn={978-989-758-384-1},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA
TI - Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks
SN - 978-989-758-384-1
IS - 2184-3236
AU - Duran-Lopez, L.
AU - Dominguez-Morales, J.
AU - Amaya-Rodriguez, I.
AU - Luna-Perejon, F.
AU - Civit-Masot, J.
AU - Vicente-Diaz, S.
AU - Linares-Barranco, A.
PY - 2019
SP - 444
EP - 448
DO - 10.5220/0008494304440448
PB - SciTePress