Assessing the Impact of Deep End-to-End Architectures in Ensemble Learning for Histopathological Breast Cancer Classification

Hasnae Zerouaoui, Ali Idri, Ali Idri, Omar El Alaoui

2022

Abstract

One of the most significant public health issues in the world and a major factor in women’s mortality is breast cancer (BC). Early diagnosis and detection can significantly improve the likelihood of survival. Therefore, this study suggests a deep end-to-end heterogeneous ensemble approach by using deep learning (DL) models for breast histological images classification tested on the BreakHis dataset. The proposed approach showed a significant increase of performances compared to their base learners. Thus, seven DL architectures (VGG16, VGG19, ResNet50, Inception_V3, Inception_ResNet_V2, Xception, and MobileNet) were trained using 5fold cross-validation. Thereafter, deep end-to-end heterogeneous ensembles of two up to seven base learners were constructed based on accuracy using majority and weighted voting. Results showed the effectiveness of deep end-to-end ensemble learning techniques for breast cancer images classification into malignant or benign. The ensembles designed with weighted voting method exceeded the others with an accuracy value reaching 93.8%, 93.4%, 93.3%, and 91.8% through the BreakHis dataset’s four magnification factors: 40X, 100X, 200X, and 400X respectively.

Paper Citation

in Harvard Style

Zerouaoui H., Idri A. and El Alaoui O. (2022). Assessing the Impact of Deep End-to-End Architectures in Ensemble Learning for Histopathological Breast Cancer Classification. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 109-118. DOI: 10.5220/0011574400003335

in Bibtex Style

@conference{kdir22,
author={Hasnae Zerouaoui and Ali Idri and Omar El Alaoui},
title={Assessing the Impact of Deep End-to-End Architectures in Ensemble Learning for Histopathological Breast Cancer Classification},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={109-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011574400003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Assessing the Impact of Deep End-to-End Architectures in Ensemble Learning for Histopathological Breast Cancer Classification
SN - 978-989-758-614-9
AU - Zerouaoui H.
AU - Idri A.
AU - El Alaoui O.
PY - 2022
SP - 109
EP - 118
DO - 10.5220/0011574400003335
PB - SciTePress