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Authors: Caleb Vununu 1 ; Suk-Hwan Lee 2 and Ki-Ryong Kwon 1

Affiliations: 1 Department of IT Convergence and Application Engineering, Pukyong National University, Busan, Republic of Korea ; 2 Department of Information Security, Tongmyong University, Busan, Republic of Korea

Keyword(s): Deep-learning, Classification of Cellular Images, Convolutional Neural Networks (CNN), HEp-2 Cell Images, Deep Convolutional Auto-Encoders (DCAE), SNPHEp-2 Dataset.

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. (More)

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Paper citation in several formats:
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 - BIOINFORMATICS, ISBN 978-989-758-398-8; ISSN 2184-4305, pages 242-245. DOI: 10.5220/0009183702420245

@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 - BIOINFORMATICS,},
year={2020},
pages={242-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009183702420245},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS,
TI - A Deep-learning based Method for the Classification of the Cellular Images
SN - 978-989-758-398-8
IS - 2184-4305
AU - Vununu, C.
AU - Lee, S.
AU - Kwon, K.
PY - 2020
SP - 242
EP - 245
DO - 10.5220/0009183702420245