loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Érika G. Assis ; Mark A. Song ; Luis E. Zárate and Cristiane N. Nobre

Affiliation: Department of Computing, Pontifical Catholic University of Minas Gerais University, Brazil

Keyword(s): Congenital Syndrome, Zika, Generative Adversarial Networks, GAN, DCGAN.

Abstract: Class imbalance is a common health care problem and often affects the performance of machine learning algorithms. Unfortunately, the minority class, generally the one with the most significant interest, has their learning affected to the detriment of the majority class. This article proposes using Deep Convolutional Generative Adversarial Networks (DCGAN) for minority class oversampling, generating synthetic instances. For this, the ’RESP-Microcephaly’ database was used, which records suspected cases of congenital alteration due to Zika virus (ZIKV) infection. The database presents unbalanced data with 2904 and 7606 instances with and without congenital alteration, respectively. To evaluate the performance of DCGAN, we compared this method with an undersampling and an oversampling approach, using SMOTE with three classification algorithms. The use of DCGAN for balancing demonstrates a significant improvement in classification indices, especially about the minority class.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.236.98.81

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Assis, É.; Song, M.; Zárate, L. and Nobre, C. (2022). Data Balancing using Deep Convolutional Generative Adversarial Networks (DCGAN) in Patients with Congenital Syndrome by Zika Virus. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 93-102. DOI: 10.5220/0010842900003123

@conference{healthinf22,
author={Érika G. Assis. and Mark A. Song. and Luis E. Zárate. and Cristiane N. Nobre.},
title={Data Balancing using Deep Convolutional Generative Adversarial Networks (DCGAN) in Patients with Congenital Syndrome by Zika Virus},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF},
year={2022},
pages={93-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010842900003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF
TI - Data Balancing using Deep Convolutional Generative Adversarial Networks (DCGAN) in Patients with Congenital Syndrome by Zika Virus
SN - 978-989-758-552-4
IS - 2184-4305
AU - Assis, É.
AU - Song, M.
AU - Zárate, L.
AU - Nobre, C.
PY - 2022
SP - 93
EP - 102
DO - 10.5220/0010842900003123
PB - SciTePress