Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks

Ferdaous Idlahcen, Pierjos Francis Colere Mboukou, Hasnae Zerouaoui, Ali Idri, Ali Idri

2022

Abstract

Cervical cancer (CxCa) is heavily swerved toward low- and middle- income countries (LMICs). Without prompt actions, the burden is anticipated to worsen by 50% from 2020 to 2040 - nearly 90% of deaths to occur in sub-Saharan Africa (SSA). Yet, uterine cervix neoplasms are readily avoidable due to a protracted latent cancer period. As it stands, deep learning (DL) is a potent solution for enhancing the early detection of cervical cancer. This work assesses and compares the performance of seven end-to-end learning architectures to automatically recognize cervical lesions and carcinoma histotypes upon hematoxylin and eosin (H&E)-stained pathology images. Pre-trained VGG16, VGG19, InceptionV3, ResNet50, MobileNetV2, InceptionResNetV2, and DenseNet201 were the implemented deep convolutional neural networks (dCNNs) throughout the present empirical analysis. Experiments are conducted on two datasets: (i) Mendeley liquid-based cytology (LBC) and (ii) The Cancer Genome Atlas (TCGA) Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma diagnostic slides. All tests were validated under a 5-fold cross-validation, with four key metrics, Scott-Knott (SK), and Borda count schemes. Both pathology data appear to promote InceptionV3 and DenseNet201. Yet, while VGG16 is a weak-performing approach for liquid-based cytology, it evinces promise in histopathology yielding 99.33% accuracy, 98.85% precision, 99.83% recall, and 99.34% F-measure.

Paper Citation

in Harvard Style

Idlahcen F., Mboukou P., Zerouaoui H. and Idri A. (2022). Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks. 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 322-329. DOI: 10.5220/0011578700003335

in Bibtex Style

@conference{kdir22,
author={Ferdaous Idlahcen and Pierjos Francis Colere Mboukou and Hasnae Zerouaoui and Ali Idri},
title={Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={322-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011578700003335},
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 - Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks
SN - 978-989-758-614-9
AU - Idlahcen F.
AU - Mboukou P.
AU - Zerouaoui H.
AU - Idri A.
PY - 2022
SP - 322
EP - 329
DO - 10.5220/0011578700003335
PB - SciTePress