Breast Cancer Detection using Deep Convolutional Neural Network

Hana Mechria, Mohamed Gouider, Khaled Hassine

2019

Abstract

Deep Convolutional Neural Network (DCNN) is considered as a popular and powerful deep learning algorithm in image classification. However, there are not many DCNN applications used in medical imaging, because large dataset for medical images is not always available. In this paper, we present two DCNN architectures, a shallow DCNN and a pre-trained DCNN model: AlexNet, to detect breast cancer from 8000 mammographic images extracted from the Digital Database for Screening Mammography. In order to validate the performance of DCNN in breast cancer detection using a big data , we carried out a comparative study with a second deep learning algorithm Stacked AutoEncoders (SAE) in terms accuracy, sensitivity and specificity. The DCNN method achieved the best results with 89.23% of accuracy, 91.11% of sensitivity and 87.75% of specificity.

Paper Citation

in Harvard Style

Mechria H., Gouider M. and Hassine K. (2019). Breast Cancer Detection using Deep Convolutional Neural Network.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 655-660. DOI: 10.5220/0007386206550660

in Bibtex Style

@conference{icaart19,
author={Hana Mechria and Mohamed Gouider and Khaled Hassine},
title={Breast Cancer Detection using Deep Convolutional Neural Network},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={655-660},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007386206550660},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Breast Cancer Detection using Deep Convolutional Neural Network
SN - 978-989-758-350-6
AU - Mechria H.
AU - Gouider M.
AU - Hassine K.
PY - 2019
SP - 655
EP - 660
DO - 10.5220/0007386206550660