A Deep-learning based Method for the Classification of the Cellular Images

Caleb Vununu, Suk-Hwan Lee, Ki-Ryong Kwon

2020

Abstract

The present work proposes a classification method for the Human Epithelial of type 2 (HEp-2) cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cellular images. The network takes the original cellular images as the inputs and learns how to reconstruct them through an encoding-decoding process in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will then be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as some of the actual deep learning based state-of-the-art methods.

Paper Citation

in Harvard Style

Vununu C., Lee S. and Kwon K. (2020). A Deep-learning based Method for the Classification of the Cellular Images. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-398-8, SciTePress, pages 242-245. DOI: 10.5220/0009183702420245

in Bibtex Style

@conference{bioinformatics20,
author={Caleb Vununu and Suk-Hwan Lee and Ki-Ryong Kwon},
title={A Deep-learning based Method for the Classification of the Cellular Images},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS},
year={2020},
pages={242-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009183702420245},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS
TI - A Deep-learning based Method for the Classification of the Cellular Images
SN - 978-989-758-398-8
AU - Vununu C.
AU - Lee S.
AU - Kwon K.
PY - 2020
SP - 242
EP - 245
DO - 10.5220/0009183702420245
PB - SciTePress