Applied Deep Learning Architectures for Breast Cancer Screening Classification

Asma Zizaan, Ali Idri, Ali Idri, Hasnae Zerouaoui

2023

Abstract

Breast cancer (BC) became the most diagnosed cancer, making it one of the deadliest diseases. Mammography is a modality used for early detection of breast cancer. The objective of the present paper is to evaluate and compare deep learning techniques applied to mammogram images. The paper conducts an experimental evaluation of eight deep Convolutional Neural Network (CNN) architectures for a binary classification of breast screening mammograms, namely VGG16, VGG19, DenseNet201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2 and Xception. This evaluation was based on four performance metrics (accuracy, precision, recall and f1-score), as well as Scott Knott statistical test and Borda count voting system. The data was extracted from the CBIS-DDSM dataset with 4000 images. And results have shown that DenseNet201 was the most efficient model for the binary classification with an accuracy of 84.27%.

Paper Citation

in Harvard Style

Zizaan A., Idri A. and Zerouaoui H. (2023). Applied Deep Learning Architectures for Breast Cancer Screening Classification. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 617-624. DOI: 10.5220/0011723700003393

in Bibtex Style

@conference{icaart23,
author={Asma Zizaan and Ali Idri and Hasnae Zerouaoui},
title={Applied Deep Learning Architectures for Breast Cancer Screening Classification},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={617-624},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011723700003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Applied Deep Learning Architectures for Breast Cancer Screening Classification
SN - 978-989-758-623-1
AU - Zizaan A.
AU - Idri A.
AU - Zerouaoui H.
PY - 2023
SP - 617
EP - 624
DO - 10.5220/0011723700003393